Prem Melville

NeurIPS Conference 2012 Conference Paper

Online L1-Dictionary Learning with Application to Novel Document Detection

• Shiva Kasiviswanathan
• Huahua Wang
• Arindam Banerjee
• Prem Melville

Given their pervasive use, social media, such as Twitter, have become a leading source of breaking news. A key task in the automated identification of such news is the detection of novel documents from a voluminous stream of text documents in a scalable manner. Motivated by this challenge, we introduce the problem of online L1-dictionary learning where unlike traditional dictionary learning, which uses squared loss, the L1-penalty is used for measuring the reconstruction error. We present an efficient online algorithm for this problem based on alternating directions method of multipliers, and establish a sublinear regret bound for this algorithm. Empirical results on news-stream and Twitter data, shows that this online L1-dictionary learning algorithm for novel document detection gives more than an order of magnitude speedup over the previously known batch algorithm, without any significant loss in quality of results. Our algorithm for online L1-dictionary learning could be of independent interest.

IJCAI Conference 2011 Conference Paper

Concept Labeling: Building Text Classifiers with Minimal Supervision

• Vijil Chenthamarakshan
• Prem Melville
• Vikas Sindhwani
• Richard D. Lawrence

The rapid construction of supervised text classification models is becoming a pervasive need across many modern applications. To reduce human-labeling bottlenecks, many new statistical paradigms (e. g. , active, semi-supervised, transfer and multi-task learning) have been vigorously pursued in recent literature with varying degrees of empirical success. Concurrently, the emergence of Web 2. 0 platforms in the last decade has enabled a world-wide, collaborative human effort to construct a massive ontology of concepts with very rich, detailed and accurate descriptions. In this paper we propose a new framework to extract supervisory information from such ontologies and complement it with a shift in human effort from direct labeling of examples in the domain of interest to the much more efficient identification of concept-class associations. Through empirical studies on text categorization problems using the Wikipedia ontology, we show that this shift allows very high-quality models to be immediately induced at virtually no cost.

ICML Conference 2009 Conference Paper

Uncertainty sampling and transductive experimental design for active dual supervision

• Vikas Sindhwani
• Prem Melville
• Richard D. Lawrence

ICML Conference 2004 Conference Paper

Diverse ensembles for active learning

• Prem Melville
• Raymond Mooney

IJCAI Conference 2003 Conference Paper

Constructing Diverse Classifier Ensembles using Artificial Training Examples

• Prem Melville
• Raymond J. Mooney

Ensemble methods like bagging and boosting that combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its generalization error. This paper presents a new method for generating ensembles that directly constructs diverse hypotheses using additional artificially-constructed training examples. The technique is a simple, general metalearner that can use any strong learner as a base classifier to build diverse committees. Experimental results using decision-tree induction as a base learner demonstrate that this approach consistently achieves higher predictive accuracy than both the base classifier and bagging (whereas boosting can occasionally decrease accuracy), and also obtains higher accuracy than boosting early in the learning curve when training data is limited.

AAAI Conference 2002 Conference Paper

Content-Boosted Collaborative Filtering for Improved Recommendations

• Prem Melville
• and Ramadass Nagarajan

Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.