The search problem in large vocabulary continuous speech recognition (LVCSR) is to locate the most probable string of words for a spoken utterance given the acoustic signal and a set of sentence models. Searching the space of possible utterances is difficult because of the large vocabulary size and the complexity imposed when long-span language models are used. This report describes an efficient search procedure and its software embodiment in a decoder, NOWAY, which has been incorporated in ABBOT, a hybrid connectionist/ hidden Markov model (HMM) LVCSR system [15]. The search algorithm is based on stack decoding and uses both likelihood- and posterior-based pruning. The use of the posterior-based phone deactivation pruning techniques is well-suited to hybrid connectionist/HMM systems because posterior phone probabilities are directly computed by the connectionist acoustic model. The single-pass decoder has been evaluate on the large vocabulary North American Business News task using a...

vi Table of Contents viii List of Figures xiii List of Tables xiv List of Symbols xvii List of Acronyms xx Chapter 1. Introduction 1 Chapter 2. Background and Related Research 8 2.1 Introduction : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 8 2.2 Generalization : : : : : : : : : : : : : : : : : : : : : : : : : : : : 9 2.3 Statistical Background : : : : : : : : : : : : : : : : : : : : : : : : 13 2.4 Regularization : : : : : : : : : : : : : : : : : : : : : : : : : : : : 16 2.5 Motivation for Combining : : : : : : : : : : : : : : : : : : : : : : 18 2.6 Historical sketch : : : : : : : : : : : : : : : : : : : : : : : : : : : 19 viii 2.6.1 Survey of Recent Literature : : : : : : : : : : : : : : : : : 19 2.6.2 Belief and Evidence Combining : : : : : : : : : : : : : : : 22 2.6.3 Economic Forecasting : : : : : : : : : : : : : : : : : : : : 23 2.6.4 Stacked Generalization : : : : : : : : : : : : : : : : : : : : 23 2.6.5 Ensemble Methods : : : : : : : : : : : : : : : : : : : : : ...

Classification of land cover based on hyperspectral data is very challenging because typically tens of classes with uneven priors are involved, the inputs are high dimensional, and there is often scarcity of labeled data. Several researchers have observed that it is often preferable to decompose a multi-class problem into multiple two-class problems, solve each such sub-problem using a suitable binary classifier, and then combine the outputs of this collection of classifiers in a suitable manner to obtain the answer to the original multi-class problem. This approach is taken by the popular error correcting output codes (ECOC) technique, as well by the binary hierarchical classifier (BHC). Classical techniques for dealing with small sample sizes include regularization of covariance matrices and feature reduction. In this paper we address the twin problems of small sample sizes and multi-class settings by proposing a feature reduction scheme that adaptively adjusts to the amount of labeled data available. This scheme can be used in conjunction with ECOC and the BHC, as well as other approaches such as round-robin classification that decompose a multi-class problem into a number of two (meta)-class problems. In particular, we develop the best-basis binary hierarchical classifier (BB-BHC) and best basis

this paper, a new invariant feature of two-dimensional contours is reported:

Training neural network classifiers for medical decision making: The effects