This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the Expectation-Maximization (EM) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker verification experiments involving 258 speakers from a phonetically balanced, continuous speech corpus (TIMIT). We propose a verification procedure using RBF and EBF networks as speaker models and show that the networks are readily applicable to verifying speakers using LP-derived cepstral coefficients as features. Experimental results show that small EBF networks with basis function parameters estimated by the EM algorithm outperform the large RBF networks trained in the conventional approach. The results also show that the equal error rate achieved by the EBF networks is about two-third of that achieved by the VQ-based speaker models. Keywords: ...

Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web search applications. Such benefits, however, are severely limited by the data sparseness problem, i.e., many queries and documents have no or very few clicks. The ranker thus cannot rely strongly on clickthrough features for document ranking. This paper presents two smoothing methods to expand clickthrough data: query clustering via Random Walk on click graphs and a discounting method inspired by the Good-Turing estimator. Both methods are evaluated on real-world data in three Web search domains. Experimental results show that the ranking models trained on smoothed clickthrough features consistently outperform those trained on unsmoothed features. This study demonstrates both the importance and the benefits of dealing with the sparseness problem in clickthrough data.

This thesis investigates feedforward neural networks in the context of classification tasks with respect to the detection of patterns that do not belong to the same categories of patterns used to train the network. This refers to the problem of the detection and/or rejection of spurious or novel patterns. In particular, the multilayer perceptron network (MLP) trained with the backpropagation algorithm is examined in this respect and different strategies for improving its performance in the detection of spurious patterns are considered. The problem is investigated from different points of view that vary from the modification of the multilayer perceptron network with different configurations that make it more intrinsically able to detect spurious information, to the introduction of novel auxiliary mechanisms which, when integrated with the MLP network, can provide an overall enhancement in the system's rejection capabilities. These different network configurations are examined with respe...

This paper presents several text-independent speaker verification experiments based on Radial Basis Function (RBF) and Elliptical Basis Function (EBF) networks. The experiments involve 76 speakers from dialect region 2 of the TIMIT and NTIMIT databases. Each speaker was modelled by a 12-input, 2-output network in which one output represents the speaker class while the other represents the anti-speaker class. The results show that both RBF and EBF networks are very robust in detecting impostors for clean speech, with the EBF networks being significantly better than the RBF networks in this respect. For clean speech, a false acceptance rate of 0.06% and a false rejection rate of 0.19% have been achieved. However, for telephone speech, the false acceptance rate and the false rejection rate are increased to 11.7% and 8.71%, respectively. It is concluded that better pre-processing techniques are required to reduce the effects of noise and channel variations. 1. INTRODUCTION In recent years...

We investigate PAC-learning in a situation in which examples (consisting of an input vector and 0/1 label) have some of the components of the input vector concealed from the learner. This is a special case of Restricted Focus of Attention (RFA) learning. Our interest here is in 1-RFA learning, where only a single component of an input vector is given, for each example. We argue that 1-RFA learning merits special consideration within the wider eld of RFA learning. It is the most restrictive form of RFA learning (so that positive results apply in general), and it models a typical \data fusion" scenario, where we have sets of observations from a number of separate sensors, but these sensors are uncorrelated sources. Within this setting we study the well-known class of linear threshold functions, the characteristic functions of Euclidean half-spaces. The sample complexity (i.e. sample-size requirement as a function of the parameters) of this learning problem is aected by the input distri...

ii To my wife, Ma'ayan, and my daughter, Shira. iii Acknowledgments Special thanks are due to: ffl Prof. Naftali Tishby for his help and guidance in carrying out this study, for the many fascinating discussions we had, and for the immense body of knowledge that I have absorbed from him during my studies.