It has been widely observed that search queries are composed in a very different style from that of the body or the title of a document. Many techniques explicitly accounting for this language style discrepancy have shown promising results for information retrieval, yet a large scale analysis on the extent of the language differences has been lacking. In this paper, we present an extensive study on this issue by examining the language model properties of search queries and the three text streams associated with each web document: the body, the title, and the anchor text. Our information theoretical analysis shows that queries seem to be composed in a way most similar to how authors summarize documents in anchor texts or titles, offering a quantitative explanation to the observations in past work.

We introduce a multi-stage ensemble framework, Error-Driven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a generalist, capable of classifying under all classes, to deliver a reasonably accurate initial category ranking given an instance. Edge then computes a confusion graph for the generalist and allocates the learning resources to train experts on relatively small groups of classes that tend to be systematically confused with one another by the generalist. The experts' votes, when invoked on a given instance, yield a reranking of the classes, thereby correcting the errors of the generalist. Our evaluations showcase the improved classification and ranking performance on several large-scale text categorization datasets. Edge is in particular efficient when the underlying learners are efficient. Our study of confusion graphs is also of independent interest.

In many multiclass learning scenarios, the number of classes is relatively large (thousands,...), or the space and time efficiency of the learning system can be crucial. We investigate two online update techniques especially suited to such problems. These updates share a sparsity preservation capacity: they allow for constraining the number of prediction connections that each feature can make. We show that one method, exponential moving average, is solving a "discrete" regression problem for each feature, changing the weights in the direction of minimizing the quadratic loss. We design the other method to improve a hinge loss subject to constraints, for better accuracy. We empirically explore the methods, and compare performance to previous indexing techniques, developed with the same goals, as well as other online algorithms based on prototype learning. We observe that while the classification accuracies are very promising, improving over previous indexing techniques, the scalability benefits are preserved.

In this paper, we discuss our work in progress towards a scalable hierarchical classification system for books using the Library of Congress subject hierarchy. We examine the characteristics of this domain which make the problem very challenging, and we look at several appropriate performance measurements. We show that both Hieron and Hierarchical Support Vector Machines perform moderately well.

A formal type of scientific and academic collaboration is coauthorship which can be represented by a coauthorship network. Coauthorship networks are among some of the largest social networks and offer us the opportunity to study the mechanisms underlying large-scale real world networks. We construct such a network for the Computer Science field covering research collaborations from 1980 to 2005, based on a large dataset of 451,305 papers authored by 283,174 distinct researchers. By mining this network, we first present a comprehensive study of the network statistical properties for a longitudinal network at the overall network level as well as for the intermediate community level. Major observations are that the database community is the best connected while the AI community is the most assortative, and that the Computer Science field as a whole shows a collaboration pattern more similar to Mathematics than to Biology. Moreover, the small world phenomenon and the scale-free degree distribution accompany the growth of the network. To study the individual collaborations, we propose a novel stochastic model, Stochastic Poisson model with Optimization Tree (Spot)to efficiently predict any increment of collaboration based on the local neighborhood structure. Spot models the non-stationary Poisson process by maximizing the log-likelihood with a tree structure. Empirical results show that Spot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration

This paper is concerned with the class imbalance problem which has been known to hinder the learning performance of classification algorithms. The problem occurs when there are significantly less number of observations of the target concept. Various real-world classification tasks, such as medical diagnosis, text categorization and fraud detection suffer from this phenomenon. The standard machine learning algorithms yield better prediction performance with balanced datasets. In this paper, we demonstrate that active learning is capable of solving the class imbalance problem by providing the learner more balanced classes. We also propose an efficient way of selecting informative instances from a smaller pool of samples for active learning which does not necessitate a search through the entire dataset. The proposed method yields an efficient querying system and allows active learning to be applied to very large datasets. Our experimental results show that with an early stopping criteria, active learning achieves a fast solution with competitive prediction performance in imbalanced data classification.

We propose a novel multiclass classification algorithm Gentle Adaptive Multiclass Boosting Learning (GAMBLE). The algorithm naturally extends the two class Gentle AdaBoost algorithm to multiclass classification by using the multiclass exponential loss and the multiclass response encoding scheme. Unlike other multiclass algorithms which reduce the K-class classification task to K binary classifications, GAMBLE handles the task directly and symmetrically, with only one committee classifier. We formally derive the GAMBLE algorithm with the quasi-Newton method, and prove the structural equivalence of the two regression trees in each boosting step. To scale up to large datasets, we utilize the generalized Query By Committee (QBC) active learning framework to focus learning on the most informative samples. Our empirical results show that with QBC-style active sample selection, we can achieve faster training time and potentially higher classification accuracy. GAMBLE's numerical superiority, structural elegance and low computation complexity make it highly competitive with state-of-the-art multiclass classification algorithms.

The class imbalance problem has been known to hinder the learning performance of classification algorithms. Various real-world classification tasks such as text categorization suffer from this phenomenon. We demonstrate that active learning is capable of solving the problem.

Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or evenshare the same name with other people. In this paper, we present an efficient framework by using two novel topic-based models, extended from Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering. Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.

The issue of seeking efficient and effective methods for classifying unstructured text in large document corpora has received much attention in recent years. Traditional document representation like bag-of-words encodes documents as feature vectors, which usually leads to sparse feature spaces with large dimensionality, thus making it hard to achieve high classification accuracies. This paper addresses the problem of classifying unstructured documents on the Web. A classification approach is proposed that utilizes traditional feature reduction techniques along with a collaborative filtering method for augmenting document feature spaces. The method produces feature spaces with an order of magnitude less features compared with a baseline bag-of-words feature selection method. Experiments on both real-world data and benchmark corpus indicate that our approach improves classification accuracy over the traditional methods for both support vector machines and AdaBoost classifiers.