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There are many practical applications where learning from single class examples is either, the only possible solution, or has a distinct performance advantage. The first case occurs when obtaining examples of a second class is difficult, e.g., classifying sites of "interest" based on web accesses. The second situation is exemplified by the gene knock-out(More)
Lexicon entry1: Data: The Lexical Access Problem consists of determining the intended sequence of words corresponding to an input sequence of phonemes (basic speech sounds) that come from a low-level phoneme recognizer. In this paper we present an information-theoretic approach based on the Minimum Message Length Criterion for solving the Lexical Access(More)
In this paper, we present a new co-training strategy that makes use of unlabelled data. It trains two predictors in parallel, with each predictor labelling the unlabelled data for training the other predictor in the next round. Both predictors are support vector machines, one trained using data from the original feature space, the other trained with new(More)
In this paper, we outline the main steps leading to the development of the winning solution for Task 2 of KDD Cup 2002 (Yeast Gene Regulation Prediction). Our unusual solution was a pair of linear classifiers in high dimensional space (∼14,000), developed with just 38 and 84 training examples, respectively, all belonging to the target class only. The(More)