Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RKHS). Two Gini distance based dependence measures are explored: Gini distance covariance and Gini distance correlation. Unlike Pearson covariance and correlation, which do not characterize independence, the above Gini distance based measures define dependence as well as independence of random variables. The test statistics are simple to calculate and do not require probability density estimation. Uniform convergence bounds and asymptotic bounds are derived for the test statistics. Comparisons with distance covariance statistics are provided. It is shown that Gini distance statistics converge faster than distance covariance statistics in the uniform convergence bounds, hence tighter upper bounds on both Type I and Type II errors. Moreover, the probability of Gini distance covariance statistic under-performing the distance covariance statistic in Type II error decreases to 0 exponentially with the increase of the sample size. Extensive experimental results are presented to demonstrate the performance of the proposed method.

Software fault localization is one of the key activities in software debugging. The program spectrum-based approach is widely used in fault localization. However, lots of program information, for example, the sequence of the execution statement and statement semantics, is missing when such an approach is utilized, which affects the performance. XGBoost is an effective learning algorithm, which can use the characteristics of the training data to build a classification tree during training. In addition, XGBoost can iteratively adjust the information value of the feature, so that the training process retains the importance information of the feature. This paper proposes applying XGBoost into fault localization utilizing information of program execution behaviors. A novel method called XGB-FL is developed, where the program spectrum information is converted into a coverage matrix to train the XGBoost model. We can get the characteristics of the data through the trained model and the importance of the program statement in the classification process. This is also the basis for judging whether the statement is likely to contain a fault. Nine representative data sets have been chosen to evaluate the performance of XGB-FL. The experimental results show that XGB-FL can generally deliver a higher performance in fault localization than those baseline techniques, in terms of precision and efficiency.

Time plays an essential role in the diffusion of information, influence and disease over networks. Usually we are only able to collect cascade data in which an infection (receiving) time of each node is recorded but without any transmission information over the network. In this paper, we infer the transmission rates among nodes by Pareto distributions. Pareto modeling has several advantages. It is naturally motivated and has a nice interpretation. The scale parameter of a Pareto distribution naturally fits into the starting time of a transition, i.e., the infection time of a parent node in the cascade data is the starting point for a transition from the parent to its receiver. The shape parameter (alpha) serves as the transition rate. The larger the alpha is, the faster the transition is and there is a higher probability for disease or information to spread in a short time period. Pareto modeling is mathematically simple and computationally easy. It has explicit solutions for the optimization problem that maximizes time-dependent pairwise transmission likelihoods between all pairs of nodes. We present three modelings with a common transmission rate, with different transmission rates and with different infection rates. Experiments on real and synthetic data show that our models accurately estimate the transmission rates and perform better than the existing method.

Molecularly targeted therapies significantly contribute to the efforts of personalized approaches for cancer diagnosis and chemotherapeutic treatment. One of a critical step to identify target molecules is to determine the most representative features for different patient's sub-groups. Breast cancer, one of the most heterogeneous cancer has five main subtypes, so accurately identify gene signatures associated with intrinsic subtypes based on currently available data resource will benefit for precision medicine. Traditional ways to identify subtype-specific targeted molecular biomarkers are based on the platforms such as microarray, or immunohistochemistry (IHC) markers. Very few studies using RNAseq data to predict cancer subtype, due to the limited data resource. Gene expression in RNAseq platform is highly correlated with the microarray. However, it shows superior benefits than microarray in many ways, such as detection of low abundance transcripts, the broader dynamic range of gene expression in both coding and non-coding genes. Therefore, RNAseq platform will become the predominant tool for transcriptome analysis in the long term. To benefit the identification of molecule targets for transcriptomic data including RNAseq and microarray expression, we developed a new feature selection method. Our results of classification accuracy for intrinsic subtypes and prognostic evaluation of selected genes outperform than the traditional PAM50 gene signature, which is derived from microarray and widely used in clinical practice.

AdaBoost is a well-known simple and effective boosting algorithm for classification. It, however, suffers from the overfitting problem in the case of overlapping class distributions and is very sensitive to label noise. To tackle both problems simultaneously, we consider the conditional risk as the modified loss function. This modification leads to two advantages: it is able to directly take into account label uncertainty with an associated label confidence; it introduces a “trustworthiness” measure on training samples via the Bayesian risk rule, hence the resulting classifier tends to have superior finite sample performance than the original AdaBoost when there is a large overlap between class conditional distributions. We scrutinize the re-weighting procedure and classifier combination rule to show its adaptive ability on the mitigation of class noise and overfitting. Extensive experimental results using synthetic data and real-world data sets from UCI machine learning repository are provided. The empirical study shows high competitiveness of the proposed method in predication accuracy and robustness when compared with the original AdaBoost and several existing robust AdaBoost algorithms.

Cancer is a disease characterized largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the state of art of microarray technologies and clinical studies, a large numbers of candidate genes are extracted. Extracting informative genes out of them is essential. In our project we aim to find the cancer driver genes using somatic mutation data and protein protein interaction data. We developed a generative mixture model coupled with Bayesian parameter estimation to estimate background mutation rates and driver probabilities of each gene as well as the proportion of drivers among all sequenced genes. We choose suitable prior distributions for modelling both driver probabilities and background mutations of each gene. We apply our method to ovarian cancer data and numerically estimated the solution. Upon convergence, we are able to discover and identify some new candidate cancer driver genes.

This paper presents a new robust EM algorithm for the finite mixture learning procedures. The proposed Spatial-EM algorithm utilizes median-based location and rank-based scatter estimators to replace sample mean and sample covariance matrix in each M step, hence enhancing stability and robustness of the algorithm. It is robust to outliers and initial values. Compared with many robust mixture learning methods, the Spatial-EM has the advantages of simplicity in implementation and statistical efficiency. We apply Spatial-EM to supervised and unsupervised learning scenarios. More specifically, robust clustering and outlier detection methods based on Spatial-EM have been proposed. We apply the outlier detection to taxonomic research on fish species novelty discovery. Two real datasets are used for clustering analysis. Compared with the regular EM and many other existing methods such as K-median, X-EM and SVM, our method demonstrates superior performance and high robustness.

Cancer is a disease driven largely by the accumulation of somatic mutations during the lifetime of a patient. Distinguishing driver mutations from passenger mutations had posed a challenge in modern cancer research. With the widespread use of microarray experiments and clinical studies, a large numbers of candidate cancer genes are produced and extracting informative genes out of them is essential. In our project we aim to find the informative genes for cancer by using mutation data from ovarian cancers. In our model we utilized the patient gene mutation profile, gene expression data and gene gene interactions network to build a graphical representation of genes and patients and construct Markov processes for mutation and patients separately. After this process, we can prioritize cancers genes automatically by looking into their scores at their stationary distributions. Comprehensive experiments show that the utilization of heterogeneous sources of information is very helpful in finding important cancer genes.

Regression is widely utilized in a variety of biological problems involving continuous outcomes. There are a number of methods for building regression models ranging from linear models to more complex nonlinear ones. While linear regression techniques can identify linear correlations between input and output, in many practical applications, the relations are nonlinear. These relations can be modeled by nonlinear regression techniques effectively. However, many models built with nonlinear techniques have limited interpretation, which is crucial in many biological problems. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features, and hence is able to provide a simple interpretation. We tested the approach on a seacoast chemical sensors dataset, a Stockori flowering time dataset, and three datasets from the UCI repository. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of conventional random forests regression. It demonstrates high potential in terms of prediction performance and interpretation ease on studying nonlinear relationships of the subjects.

BACKGROUND: In drug discovery and development, it is crucial to determine which conformers (instances) of a given molecule are responsible for its observed biological activity and at the same time to recognize the most representative subset of features (molecular descriptors). Due to experimental difficulty in obtaining the bioactive conformers, computational approaches such as machine learning techniques are much needed. Multiple Instance Learning (MIL) is a machine learning method capable of tackling this type of problem. In the MIL framework, each instance is represented as a feature vector, which usually resides in a high-dimensional feature space. The high dimensionality may provide significant information for learning tasks, but at the same time it may also include a large number of irrelevant or redundant features that might negatively affect learning performance. Reducing the dimensionality of data will hence facilitate the classification task and improve the interpretability of the model. RESULTS: In this work we propose a novel approach, named multiple instance learning via joint instance and feature selection. The iterative joint instance and feature selection is achieved using an instance-based feature mapping and 1-norm regularized optimization. The proposed approach was tested on four biological activity datasets. CONCLUSIONS: The empirical results demonstrate that the selected instances (prototype conformers) and features (pharmacophore fingerprints) have competitive discriminative power and the convergence of the selection process is also fast.

BACKGROUND: In the context of drug discovery and development, much effort has been exerted to determine which conformers of a given molecule are responsible for the observed biological activity. In this work we aimed to predict bioactive conformers using a variant of supervised learning, named multiple-instance learning. A single molecule, treated as a bag of conformers, is biologically active if and only if at least one of its conformers, treated as an instance, is responsible for the observed bioactivity; and a molecule is inactive if none of its conformers is responsible for the observed bioactivity. The implementation requires instance-based embedding, and joint feature selection and classification. The goal of the present project is to implement multiple-instance learning in drug activity prediction, and subsequently to identify the bioactive conformers for each molecule. METHODS: We encoded the 3-dimensional structures using pharmacophore fingerprints which are binary strings, and accomplished instance-based embedding using calculated dissimilarity distances. Four dissimilarity measures were employed and their performances were compared. 1-norm SVM was used for joint feature selection and classification. The approach was applied to four data sets, and the best proposed model for each data set was determined by using the dissimilarity measure yielding the smallest number of selected features. RESULTS: The predictive abilities of the proposed approach were compared with three classical predictive models without instance-based embedding. The proposed approach produced the best predictive models for one data set and second best predictive models for the rest of the data sets, based on the external validations. To validate the ability of the proposed approach to find bioactive conformers, 12 small molecules with co-crystallized structures were seeded in one data set. 10 out of 12 co-crystallized structures were indeed identified as significant conformers using the proposed approach. CONCLUSIONS: The proposed approach was proven not to suffer from overfitting and to be highly competitive with classical predictive models, so it is very powerful for drug activity prediction. The approach was also validated as a useful method for pursuit of bioactive conformers.

There are a vast number of biology related research problems involving a combination of multiple sources of data to achieve a better understanding of the underlying problems. It is important to select and interpret the most important information from these sources. Thus it will be beneficial to have a good algorithm to simultaneously extract rules and select features for better interpretation of the predictive model. We propose an efficient algorithm, Joint Rule Extraction and Feature Selection (JRF), based on 1-norm regularized random forests. JRF simultaneously extracts a small number of rules generated by random forests and selects important features. We applied JRF to several drug activity prediction and microarray data sets. JRF is capable of producing performance comparable with state-of-the-art prediction algorithms using a small number of decision rules. Some of the decision rules are biologically significant.

BACKGROUND: It is commonly believed that including domain knowledge in a prediction model is desirable. However, representing and incorporating domain information in the learning process is, in general, a challenging problem. In this research, we consider domain information encoded by discrete or categorical attributes. A discrete or categorical attribute provides a natural partition of the problem domain, and hence divides the original problem into several non-overlapping sub-problems. In this sense, the domain information is useful if the partition simplifies the learning task. The goal of this research is to develop an algorithm to identify discrete or categorical attributes that maximally simplify the learning task. RESULTS: We consider restructuring a supervised learning problem via a partition of the problem space using a discrete or categorical attribute. A naive approach exhaustively searches all the possible restructured problems. It is computationally prohibitive when the number of discrete or categorical attributes is large. We propose a metric to rank attributes according to their potential to reduce the uncertainty of a classification task. It is quantified as a conditional entropy achieved using a set of optimal classifiers, each of which is built for a sub-problem defined by the attribute under consideration. To avoid high computational cost, we approximate the solution by the expected minimum conditional entropy with respect to random projections. This approach is tested on three artificial data sets, three cheminformatics data sets, and two leukemia gene expression data sets. Empirical results demonstrate that our method is capable of selecting a proper discrete or categorical attribute to simplify the problem, i.e., the performance of the classifier built for the restructured problem always beats that of the original problem. CONCLUSIONS: The proposed conditional entropy based metric is effective in identifying good partitions of a classification problem, hence enhancing the prediction performance.

Explosive compounds such as TNT and RDX have various toxicological effects on the natural environment. The goal of the earthworm microarray experiment is to unearth the biomarker for toxicity evaluation. We propose a novel recursive gene selection method which can handle the multi-class setting effectively and efficiently. The selection is performed iteratively. In each iteration, a linear multi-class classifier is trained using 1-norm regularization, which leads to sparse weight vectors, i.e., many feature weights are exactly zero. Those zero-weight features are eliminated in the next iteration. The empirical results demonstrate that the selected features (genes) have very competitive discriminative power. In addition, the selection process has fast rate of convergence.

BACKGROUND: Microarray technology has made it possible to simultaneously monitor the expression levels of thousands of genes in a single experiment. However, the large number of genes greatly increases the challenges of analyzing, comprehending and interpreting the resulting mass of data. Selecting a subset of important genes is inevitable to address the challenge. Gene selection has been investigated extensively over the last decade. Most selection procedures, however, are not sufficient for accurate inference of underlying biology, because biological significance does not necessarily have to be statistically significant. Additional biological knowledge needs to be integrated into the gene selection procedure. RESULTS: We propose a general framework for gene ranking. We construct a bipartite graph from the Gene Ontology (GO) and gene expression data. The graph describes the relationship between genes and their associated molecular functions. Under a species condition, edge weights of the graph are assigned to be gene expression level. Such a graph provides a mathematical means to represent both species-independent and species-dependent biological information. We also develop a new ranking algorithm to analyze the weighted graph via a kernelized spatial depth (KSD) approach. Consequently, the importance of gene and molecular function can be simultaneously ranked by a real-valued measure, KSD, which incorporates the global and local structure of the graph. Over-expressed and under-regulated genes also can be separately ranked. CONCLUSION: The gene-function bigraph integrates molecular function annotations into gene expression data. The relevance of genes is described in the graph (through a common function). The proposed method provides an exploratory framework for gene data analysis.

Statistical depth functions provide from the "deepest" point a "center-outward ordering" of multidimensional data. In this sense, depth functions can measure the "extremeness" or "outlyingness" of a data point with respect to a given data set. Hence, they can detect outliers--observations that appear extreme relative to the rest of the observations. Of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tractability. In this article, we propose a novel statistical depth, the kernelized spatial depth (KSD), which generalizes the spatial depth via positive definite kernels. By choosing a proper kernel, the KSD can capture the local structure of a data set while the spatial depth fails. We demonstrate this by the half-moon data and the ring-shaped data. Based on the KSD, we propose a novel outlier detection algorithm, by which an observation with a depth value less than a threshold is declared as an outlier. The proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. It applies to a one-class learning setting, in which "normal" observations are given as the training data, as well as to a missing label scenario, where the training set consists of a mixture of normal observations and outliers with unknown labels. We give upper bounds on the false alarm probability of a depth-based detector. These upper bounds can be used to determine the threshold. We perform extensive experiments on synthetic data and data sets from real applications. The proposed outlier detector is compared with existing methods. The KSD outlier detector demonstrates a competitive performance.

It is estimated that less than 10 percent of the world's species have been described, yet species are being lost daily due to human destruction of natural habitats. The job of describing the earth's remaining species is exacerbated by the shrinking number of practicing taxonomists and the very slow pace of traditional taxonomic research. In this article, we tackle, from a novelty detection perspective, one of the most important and challenging research objectives in taxonomy ­ new species identification. We propose a unique and efficient novelty detection framework based on statistical depth functions. Statistical depth functions provide from the "deepest" point a "center-outward ordering" of multidimensional data. In this sense, they can detect observations that appear extreme relative to the rest of the observations, i.e., novelty. Of the various statistical depths, the spatial depth is especially appealing because of its computational efficiency and mathematical tractability. We propose a novel statistical depth, the kernelized spatial depth (KSD) that generalizes the spatial depth via positive definite kernels. By choosing a proper kernel, the KSD can capture the local structure of a data set while the spatial depth fails. Observations with depth values less than a threshold are declared as novel. The proposed algorithm is simple in structure: the threshold is the only one parameter for a given kernel. We give an upper bound on the false alarm probability of a depth-based detector, which can be used to determine the threshold. Experimental study demonstrates its excellent potential in new species discovery.

Motif discovery from bio sequences, a challenging task both experimentally and computationally, has been a topic of immense study in recent years. In this paper, we formulate the motif discovery problem as a multiple-instance problem and employ a multiple-instance learning method, the MILES method, to identify motif from biological sequences. Each sequence is mapped into a feature space defined by instances in training sequences with a novel instance-bag similarity measure. We employ I-norm SVM to select important features and construct classifiers simultaneously. These high-ranked features correspond to discovered motifs. We apply this method to discover transcriptional factor binding sites in promoters, a typical motif finding problem in biology, and show that the method is at least comparable to existing methods

Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (Multiple-Instance Learning via Embedded instance Selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty.

When a parallel manipulator suffers from failures, its performance can be significantly affected. Thus, fault tolerance is essential for task-critical applications or applications in which maintenance is hard to implement. In this paper, we consider three types of common strut failures corresponding to stuck joints, unactuated actuators, or the complete loss of struts, respectively. The impacts of different failures on the kinematics of a manipulator are examined, and the task space redundancy and kinematic redundancies are used to help overcome these failures. In addition, local measures of fault tolerance and their properties are analyzed. These measures can be helpful in architecture design and path planning

Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.

It is estimated that ninety percent of the world's species have yet to be discovered and described. The main reason for the slow pace of new species description is that the science of taxonomy, as traditionally practiced, can be very laborious. To formally describe a new species, taxonomists have to manually gather and analyze data from large numbers of specimens, often from broad geographic areas, and identify the smallest subset of external body characters that uniquely diagnoses the new species as distinct from all its known relatives. In this paper, we use an automated feature selection and classification approach to address the taxonomic impediment in new species discovery. The experiments on a taxonomic problem involving species of suckers in the genus Carpiodes demonstrate promising results.

Histology is the science of understanding the structure of animals and plants, and studying the functional implications of biological structures. In this paper, we propose a statistical modeling approach to human histological image categorization. Texture features of the images are characterized by localized Gabor filters. The probabilistic distribution of the texture patterns from each category is approximated by a finite Gaussian mixture model. Expectation maximization (EM) procedure and minimum message length (MML) principle are used to perform density estimation and model selection, respectively. Component-wise EM and weak component annihilation are applied to avoid the drawbacks of the standard EM. Experimental validation is provided based on images from different organs and parts of the body.

We present a novel spectral clustering method that enables users to incorporate prior knowledge of the size of clusters into the clustering process. The cost function, which is named size regularized cut (SRcut), is defined as the sum of the inter-cluster similarity and a regularization term measuring the relative size of two clusters. Finding a partition of the data set to minimize SRcut is proved to be NP-complete. An approximation algorithm is proposed to solve a relaxed version of the optimization problem as an eigenvalue problem. Evaluations over different data sets demonstrate that the method is not sensitive to outliers and performs better than normalized cut.

In a typical content-based image retrieval (CBIR) system, target images (images in the database) are sorted by feature similarities with respect to the query. Similarities among target images are usually ignored. This paper introduces a new technique, cluster-based retrieval of images by unsupervised learning (CLUE), for improving user interaction with image retrieval systems by fully exploiting the similarity information. CLUE retrieves image clusters by applying a graph-theoretic clustering algorithm to a collection of images in the vicinity of the query. Clustering in CLUE is dynamic. In particular, clusters formed depend on which images are retrieved in response to the query. CLUE can be combined with any real-valued symmetric similarity measure (metric or nonmetric). Thus, it may be embedded in many current CBIR systems, including relevance feedback systems. The performance of an experimental image retrieval system using CLUE is evaluated on a database of around 60,000 images from COREL. Empirical results demonstrate improved performance compared with a CBIR system using the same image similarity measure. In addition, results on images returned by Google's Image Search reveal the potential of applying CLUE to real-world image data and integrating CLUE as a part of the interface for keyword-based image retrieval systems.

This paper investigates the connection between additive fuzzy systems and kernel machines. We prove that, under quite general conditions, these two seemingly quite distinct models are essentially equivalent. As a result, algorithms based upon support vector (SV) learning are proposed to build fuzzy systems for classification and function approximation. The performance of the proposed algorithm is illustrated using extensive experimental results.

The performance of most region-based image retrieval systems depend critically on the accuracy of object segmentation. We propose a region matching approach, unified feature matching (UFM), which greatly increases the robustness of the retrieval system against segmentation related uncertainties. In our retrieval system, an image is represented by a set of segmented regions each of which is characterized by a fuzzy feature reflecting color, texture, and shape properties. The resemblance between two images is then defined as the overall similarity between two families of fuzzy features, and quantified by the UFM measure. The system has been tested on a database of about 60,000 general-purpose images. Experimental results demonstrate improved accuracy and robustness.

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.

In many applications, the manipulations require only part of the degrees of freedom (DOFs) of the end-effector, or some DOFs are more important than the rest. We name these applications prioritized manipulations. The end-effector's DOFs are divided into those which are critical and must be controlled as precisely as possible, and those which have loose specifications, so their tracking performance can be traded off to achieve other needs. In this paper, for the class of general constrained rigid multibody systems (including passive joints and multiple closed kinematic loops), we derive a formulation for partitioning the task space into major and secondary task directions, and finding the velocity and static force mappings that precisely accomplish the major task and optimize some secondary goals such as reliability enhancement, obstacle and singularity avoidance, fault tolerance, or joint limit avoidance. The major task and secondary goals need to be specified in term of velocities/forces. In addition, a framework is developed to handle two kinds of common actuator failures, torque failure and position failure, by reconfiguring the differential kinematics and static force models. The techniques are tested on a 6-DOF parallel robot. Experimental results illustrate that the approach is practical and yields good performance.

In a number of contexts relevant to control problems, including estimation of robot dynamics, covariance, and smart structure mass and stiffness matrices, we need to solve an over-determined set of linear equations AX /spl ap/ B with the constraint that the matrix X be symmetric and positive definite. In the classical least squares method, the measurements of A are assumed to be free of error. Hence, all errors are confined to B. Thus, the "optimal" solution is given by minimizing /spl par/AX - B/spl par//sub F//sup 2/. However, this assumption is often impractical. Sampling errors, modeling errors, and, sometimes, human errors bring inaccuracies to A as well. We introduce a different optimization criterion, based on area, which takes the errors in both A and B into consideration. The analytic expression of the global optimizer is derived. The algorithm is applied to identify the joint space mass-inertia matrix of a Gough-Stewart platform. Experimental results indicate that the new approach is practical, and improves performance.

This paper proposes a fuzzy logic approach, UFM (unified feature matching), for region-based image retrieval. In our retrieval system, an image is represented by a set of segmented regions, each of which is characterized by a fuzzy feature (fuzzy set) reflecting color, texture, and shape properties. As a result, an image is associated with a family of fuzzy features corresponding to regions. Fuzzy features naturally characterize the gradual transition between regions (blurry boundaries) within an image and incorporate the segmentation-related uncertainties into the retrieval algorithm. The resemblance of two images is then defined as the overall similarity between two families of fuzzy features and quantified by a similarity measure, UFM measure, which integrates properties of all the regions in the images. Compared with similarity measures based on individual regions and on all regions with crisp-valued feature representations, the UFM measure greatly reduces the influence of inaccurate segmentation and provides a very intuitive quantification. The UFM has been implemented as a part of our experimental SIMPLIcity image retrieval system. The performance of the system is illustrated using examples from an image database of about 60,000 general-purpose images.

We propose FIRM (Fuzzily Integrated Region Matching), an efficient and robust similarity measure for region-based image retrieval. Each image in our retrieval system is represented by a set of regions that are characterized by fuzzy sets. The FIRM measure, representing the overall similarity between two images, is defined as the similarity between two families of fuzzy sets. Compared with similarity measures based on individual regions and on all regions with crisp feature representations, our approach greatly reduces the influence of inaccurate segmentation. Experimental results based on a database of about 200,000 general-purpose images demonstrate improved accuracy, robustness, and high speed.

By exploiting properties of the joint space mass-inertia matrix of flexure jointed hexapods, a decoupling method is proposed. The decoupling method, through a static input-output mapping, transforms the highly coupled 6 input 6 output dynamics into 6 independent single-input single-output (SISO) channels. Controls for these SISO channels are far simpler than their multiple-input multiple-output counterparts. Prior decoupling control algorithms imposed severe constraints on the allowable geometry, workspace, and payload. The paper derives a new algorithm which removes these constraints, thus greatly expanding the applications. Based on the new decoupling algorithm, an identification algorithm is introduced to identify the joint space mass-inertia matrix using payload accelerations and base forces. This algorithm can be used for precision payload calibration, this improving performance and removing the labor required to design the control for different payloads. The new decoupling algorithm is experimentally compared to earlier techniques. These experimental results indicate that the new approach is practical and improves performance.

Efficient second order algorithm for training feedforward neural networks is presented. The algorithm has a similar convergence rate as the Lavenberg-Marquardt (LM) method and it is less computationally intensive and requires less memory. This is especially important for large neural networks where the LM algorithm becomes impractical. Algorithm was verified with several examples.