Original Contribution: Stacked generalization
Neural Networks
A maximum entropy approach to natural language processing
Computational Linguistics
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to Parse Natural Language with Maximum Entropy Models
Machine Learning - Special issue on natural language learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
Training conditional random fields via gradient tree boosting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature-rich part-of-speech tagging with a cyclic dependency network
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Conditional structure versus conditional estimation in NLP models
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Composition of conditional random fields for transfer learning
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Practical use of non-local features for statistical spoken language understanding
Computer Speech and Language
On the Use of Structures for Spoken Language Understanding: A Two-Step Approach
IEICE - Transactions on Information and Systems
Search-based structured prediction
Machine Learning
Ensemble Strategies for Classifying Hyperspectral Remote Sensing Data
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Simulated Iterative Classification A New Learning Procedure for Graph Labeling
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Multi-Scale Multi-Resolution Stacked Sequential Learning
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
Multi-Scale Multi-Resolution Stacked Sequential Learning
Proceedings of the 2009 conference on Artificial Intelligence Research and Development: Proceedings of the 12th International Conference of the Catalan Association for Artificial Intelligence
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Modelling and analyzing multimodal dyadic interactions using social networks
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Classifying Objects at Different Sizes with Multi-Scale Stacked Sequential Learning
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Multi-scale stacked sequential learning
Pattern Recognition
A stacked sub-word model for joint Chinese word segmentation and part-of-speech tagging
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Multi-class multi-scale stacked sequential learning
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
ACM Transactions on Asian Language Information Processing (TALIP)
Dependent binary relevance models for multi-label classification
Pattern Recognition
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We describe a new sequential learning scheme called "stacked sequential learning". Stacked sequential learning is a meta-learning algorithm, in which an arbitrary base learner is augmented so as to make it aware of the labels of nearby examples. We evaluate the method on several "sequential partitioning problems", which are characterized by long runs of identical labels. We demonstrate that on these problems, sequential stacking consistently improves the performance of nonsequential base learners; that sequential stacking often improves performance of learners (such as CRFs) that are designed specifically for sequential tasks; and that a sequentially stacked maximum-entropy learner generally outperforms CRFs.