A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
Ginisupport vector machines for segmental minimum Bayes risk decoding of continuous speech
Computer Speech and Language
Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression
The Journal of Machine Learning Research
Data & Knowledge Engineering
Component-based discriminative classification for hidden Markov models
Pattern Recognition
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Forward Decoding Kernel Machines (FDKM) combine largemargin classifiers with Hidden Markov Models (HMM) for Maximum a Posteriori (MAP) adaptive sequence estimation. State transitions in the sequence are conditioned on observed data using a kernel-based probability model, and forward decoding of the state transition probabilities with the sum-product algorithm directly produces the MAP sequence. The parameters in the probabilistic model are trained using a recursive scheme that maximizes a lower bound on the regularized cross-entropy. The recursion performs an expectation step on the outgoing state of the transition probability model, using the posterior probabilities produced by the previous maximization step. Similar to Expectation-Maximization (EM), the FDKM recursion deals effectively with noisy and partially labeled data.We also introduce a multi-class support vector machine for sparse conditional probability regression, GiniSVM based on a quadratic formulation of entropy. Experiments with benchmark classification data show that GiniSVM generalizes better than other multi-class SVM techniques. In conjunction with FDKM, GiniSVM produces a sparse kernel expansion of state transition probabilities, with drastically fewer non-zero coefficients than kernel logistic regression. Preliminary evaluation of FDKM with GiniSVM on a subset of the TIMIT speech database reveals significant improvements in phoneme recognition accuracy over other SVM and HMM techniques.