The nature of statistical learning theory
The nature of statistical learning theory
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Exploiting Hierarchy in Text Categorization
Information Retrieval
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning Additive Models Online with Fast Evaluating Kernels
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
On the use of support vector machines for phonetic classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
On the generalization ability of on-line learning algorithms
IEEE Transactions on Information Theory
Hierarchical classification: combining Bayes with SVM
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discriminative keyword spotting
Speech Communication
A semi-dependent decomposition approach to learn hierarchical classifiers
Pattern Recognition
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
Large Margin Hierarchical Classification with Mutually Exclusive Class Membership
The Journal of Machine Learning Research
Poisson-based inference for perturbation models in adaptive spelling training
International Journal of Artificial Intelligence in Education
Keyword spotting exploiting Long Short-Term Memory
Speech Communication
SPREAD: sound propagation and perception for autonomous agents in dynamic environments
Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation
SR-NBS: A fast sparse representation based N-best class selector for robust phoneme classification
Engineering Applications of Artificial Intelligence
Extension of a Kernel-Based Classifier for Discriminative Spoken Keyword Spotting
Neural Processing Letters
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We present an algorithmic framework for phoneme classification where the set of phonemes is organized in a predefined hierarchical structure. This structure is encoded via a rooted tree which induces a metric over the set of phonemes. Our approach combines techniques from large margin kernel methods and Bayesian analysis. Extending the notion of large margin to hierarchical classification, we associate a prototype with each individual phoneme and with each phonetic group which corresponds to a node in the tree. We then formulate the learning task as an optimization problem with margin constraints over the phoneme set. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent phonemes in the phonetic hierarchy. We describe a new online algorithm for solving the hierarchical classification problem and provide worst-case loss analysis for the algorithm. We demonstrate the merits of our approach by applying the algorithm to synthetic data and as well as speech data.