Object detection is a widely-used computer vision task, in which we identify a bounding box around each object in an image and classify the object into one of pre-defined classes. Single Shot MultiBox Detector (SSD) is a real-time object detector based on a single convolutional neural network. SSD is popular and known for high speed and accuracy, but its black-box nature is not ignorable when it is applied to critical systems. In this paper, we propose Contrastive Relevance Propagation (CRP), an extension of Layer-wise Relevance Propagation (LRP) tailored for SSD. CRP can consistently deal with SSD's heterogeneous output, i.e. confidences for object classes and location offsets, and create a heatmap that highlights a crucial part of the input, which is not available with a standard use of LRP. By experiments with the Pascal VOC 2012 dataset, we confirmed the quality of heatmaps created by CRP, and with such heatmaps, we conducted a simple analysis on prediction errors made by SSD.

One practical inconvenience in frequent pattern mining is that it often yields a flood of common or uninformative patterns, and thus we should carefully adjust the minimum support. To alleviate this inconvenience, based on FP-growth, this paper proposes RP-growth, an efficient algorithm for top-k mining of discriminative patterns which are highly relevant to the class of interest. RP-growth conducts a branch-and-bound search using anti-monotonic upper bounds of the relevance scores such as F-score and χ2, and the pruning in branch-and-bound search is successfully translated to minimum support raising, a standard, easy-to-implement pruning strategy for top-k mining. Furthermore, by introducing the notion called weakness and an additional, aggressive pruning strategy based on weakness, RP-growth efficiently finds k patterns of wide variety and high relevance to the class of interest. Experimental results on text classification exhibit the efficiency and the usefulness of RP-growth.

As stated in the building block hypothesis, we expect genetic algorithms (GAs) to create building blocks (BBs) and combine them appropriately in the evolutionary process. However, such BBs are often destroyed by unwanted crossovers, soon after they are created. Also, we may suffer from a "loose" encoding of chromosomes since BBs are in general unknown. In this paper, we propose a framework named GAP (GA with patterns), in which key patterns are extracted from significantly "good" chromosomes and protect such key patterns against unwanted crossover. GAP is applicable to optimization problems with fixed-point encoding and permutation encoding in a uniform fashion, and unlike perturbation-based linkage learning methods, GAP does not require extra fitness evaluations. Experimental results with the royal road problems and traveling salesman problems show the performance improvement of GAP over standard GAs.

In knowledge discovery from real-valued time series, discretization is often a key preprocessing that extends the applicability of sophisticated tools for symbolic data mining or logic-based machine learning. For finding meaningful discrete values that can be directly translated into some intuitive symbols, this paper proposes a novel discretization method based on density estimation using a two-dimensional (measurement vs. time) histogram of variable-width bins. We extend Kontkanen and Myllymaki's histogram construction method into our two dimensional case, keeping the efficiency brought by dynamic programming. Experimental results with artificial and real datasets show the robustness and the usefulness of the proposed method.

In a knowledge discovery process, interpretation and evaluation of the mined results are indispensable in practice. In the case of data clustering, however, it is often difficult to see in what aspect each cluster has been formed. This paper proposes a method for automatic and objective characterization or "verbalization" of the clusters obtained by mixture models, in which we collect conjunctions of propositions (attribute value pairs) that help us interpret or evaluate the clusters. The proposed method provides us with a new, in-depth and consistent tool for cluster interpretation/evaluation, and works for various types of datasets including continuous attributes and missing values. Experimental results exhibit the utility of the proposed method, and the importance of the feedbacks from the interpretation/evaluation step.

Mode-directed tabling amounts to using table modes to control what arguments are used in variant checking of subgoals and how answers are tabled. A mode can be min, max, + (input), (output), or nt (non-tabled). While the traditional table-all approach to tabling is good for finding all answers, mode-directed tabling is well suited to dynamic programming problems that require selective answers. In this paper, we present three application examples of mode-directed tabling, namely, (1) hydraulic system planning, a dynamic programming problem, (2) the Viterbi algorithm in PRISM, a probabilistic logic reasoning and learning system, and (3) constraint checking in evaluating Answer Set Programs (ASP). For the Viterbi application, the feature of enabling a cardinality limit in a table mode declaration plays an important role. For a PRISM program and a set of data, the explanations may be too large to be completely stored and the cardinality limit allows for Viterbi inference based on a subset of explanations. The mode nt, which specifies an argument that can participate in the computation of a tabled predicate but is never tabled either in subgoal or answer tabling, is useful in constraint checking for the Hamilton cycle problem encoded as an ASP. These examples demonstrate the usefulness of mode-directed tabling.

Abstract PRISM is an extension of Prolog with probabilistic predicates and built-in support for expectation-maximization learning. Constraint Handling Rules (CHR) is a high-level programming language based on multi-headed multiset rewrite rules. In this paper, we introduce a new probabilistic logic formalism, called CHRiSM, based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of “chance rules”. The underlying PRISM system can then be used for several probabilistic inference tasks, including probability computation and parameter learning. We define the CHRiSM language in terms of syntax and operational semantics, and illustrate it with examples. We define the notion of ambiguous programs and define a distribution semantics for unambiguous programs. Next, we describe an implementation of CHRiSM, based on CHR(PRISM). We discuss the relation between CHRiSM and other probabilistic logic programming languages, in particular PCHR. Finally, we identify potential application domains.

One crucial issue in genetic programming (GP) is how to acquire promising building blocks efficiently. In this paper, we propose a GP method (called GPTM, GP with Tree Mining) which protects the subtrees repeatedly appearing in superior individuals. Currently GPTM utilizes a FREQT-like efficient data mining method to find such subtrees. GPTM is evaluated by three benchmark problems, and the results indicate that GPTM is comparable to or better than POLE, one of the most advanced probabilistic model building GP methods, and finds the optimal individual earlier than the standard GP and POLE.

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. definite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics, possible world semantics with a probability distribution which is unconditionally applicable to arbitrary logic programs including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM algorithm, the graphical EM algorithm, that runs for a class of parameterized logic programs representing sequential decision processes where each decision is exclusive and independent. It runs on a new data structure called support graphs describing the logical relationship between observations and their explanations, and learns parameters by computing inside and outside probability generalized for logic programs. The complexity analysis shows that when combined with OLDT search for all explanations for observations, the graphical EM algorithm, despite its generality, has the same time complexity as existing EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside algorithm for PCFGs, and the one for singly connected Bayesian networks that have been developed independently in each research field. Learning experiments with PCFGs using two corpora of moderate size indicate that the graphical EM algorithm can significantly outperform the Inside-Outside algorithm.