Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Towards language independent automated learning of text categorization models
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Machine Learning - Special issue on learning with probabilistic representations
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Distributional clustering of words for text classification
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Using Feature Hierarchies in Bayesian Network Learning
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Clustering categorical data: an approach based on dynamical systems
The VLDB Journal — The International Journal on Very Large Data Bases
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Using DAML+OIL to classify intrusive behaviours
The Knowledge Engineering Review
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning decision trees with taxonomy of propositionalized attributes
Pattern Recognition
Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers
Expert Systems with Applications: An International Journal
RNBL-MN: a recursive naive bayes learner for sequence classification
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
TwitAg: a multi-agent feature selection and recommendation framework for twitter
PRIMA'10 Proceedings of the 13th international conference on Principles and Practice of Multi-Agent Systems
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In many machine learning applications that deal with sequences, there is a need for learning algorithms that can effectively utilize the hierarchical grouping of words. We introduce Word Taxonomy guided Naive Bayes Learner for the Multinomial Event Model (WTNBL-MN) that exploits word taxonomy to generate compact classifiers, and Word Taxonomy Learner (WTL) for automated construction of word taxonomy from sequence data. WTNBL-MN is a generalization of the Naive Bayes learner for the Multinomial Event Model for learning classifiers from data using word taxonomy. WTL uses hierarchical agglomerative clustering to cluster words based on the distribution of class labels that co-occur with the words. Our experimental results on protein localization sequences and Reuters text show that the proposed algorithms can generate Naive Bayes classifiers that are more compact and often more accurate than those produced by standard Naive Bayes learner for the Multinomial Model.