Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Original Contribution: Stacked generalization
Neural Networks
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Statistical methods for speech recognition
Statistical methods for speech recognition
Natural gradient works efficiently in learning
Neural Computation
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Multidimensional curve classification using passing—through regions
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Overall risk criterion estimation of hidden Markov model parameters
Speech Communication
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Forward Decoding Kernel Machines: A Hybrid HMM/SVM Approach to Sequence Recognition
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Signal Processing - Special issue: Genomic signal processing
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Shape Descriptor Based on Circular Hidden Markov Model
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Using Hidden Markov Models and Wavelets for Face Recognition
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Text classification using string kernels
The Journal of Machine Learning Research
Shallow parsing using specialized hmms
The Journal of Machine Learning Research
Discriminant Analysis of Stochastic Models and Its Application to Face Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Fisher scores and appearance based features for face recognition
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Hidden Markov Model Based Continuous Online Gesture Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Rational Kernels: Theory and Algorithms
The Journal of Machine Learning Research
Temporal classification: extending the classification paradigm to multivariate time series
Temporal classification: extending the classification paradigm to multivariate time series
Architectures for speech-to-speech translation using finite-state models
S2S '02 Proceedings of the ACL-02 workshop on Speech-to-speech translation: algorithms and systems - Volume 7
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Acoustic Modelling Using Continuous Rational Kernels
Journal of VLSI Signal Processing Systems
Issues in stacked generalization
Journal of Artificial Intelligence Research
Similarity-based classification of sequences using hidden Markov models
Pattern Recognition
Face recognition based on multi-class mapping of Fisher scores
Pattern Recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Experiments on the application of IOHMMs to model financial returns series
IEEE Transactions on Neural Networks
Clustering-Based Construction of Hidden Markov Models for Generative Kernels
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Information theoretical Kernels for generative embeddings based on hidden Markov models
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
A generative score space for statistical dialog characterization in social signalling
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Robust pathological voice detection based on component information from HMM
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
The dissimilarity representation for structural pattern recognition
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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Hidden Markov models (HMMs) have been successfully applied to a wide range of sequence modeling problems. In the classification context, one of the simplest approaches is to train a single HMM per class. A test sequence is then assigned to the class whose HMM yields the maximum a posterior (MAP) probability. This generative scenario works well when the models are correctly estimated. However, the results can become poor when improper models are employed, due to the lack of prior knowledge, poor estimates, violated assumptions or insufficient training data. To improve the results in these cases we propose to combine the descriptive strengths of HMMs with discriminative classifiers. This is achieved by training feature-based classifiers in an HMM-induced vector space defined by specific components of individual hidden Markov models. We introduce four major ways of building such vector spaces and study which trained combiners are useful in which context. Moreover, we motivate and discuss the merit of our method in comparison to dynamic kernels, in particular, to the Fisher Kernel approach.