Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Ultraconservative online algorithms for multiclass problems
The Journal of Machine Learning Research
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Non-projective dependency parsing using spanning tree algorithms
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
CoNLL-X shared task on multilingual dependency parsing
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Hi-index | 0.00 |
We investigate a family of update methods for online machine learning algorithms for cost-sensitive multiclass and structured classification problems. The update rules are based on multinomial logistic models. The most interesting question for such an approach is how to integrate the cost function into the learning paradigm. We propose a number of solutions to this problem. To demonstrate the applicability of the algorithms, we evaluated them on a number of classification tasks related to incremental dependency parsing. These tasks were conventional multiclass classification, hiearchical classification, and a structured classification task: complete labeled dependency tree prediction. The performance figures of the logistic algorithms range from slightly lower to slightly higher than margin-based online algorithms.