A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Feature Generation Using General Constructor Functions
Machine Learning
Analysis and Recognition of Asian Scripts - the State of the Art
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Feature Kernel Functions: Improving SVMs Using High-Level Knowledge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature generation for text categorization using world knowledge
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Analytical features: a knowledge-based approach to audio feature generation
EURASIP Journal on Audio, Speech, and Music Processing
Interactive feature space construction using semantic information
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
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Choosing good features to represent objects can be crucial to the success of supervised machine learning algorithms. Good high-level features are those that concentrate information about the classification task. Such features can often be constructed as non-linear combinations of raw or native input features such as the pixels of an image. Using many nonlinear combinations, as do SVMs, can dilute the classification information necessitating many training examples. On the other hand, searching even a modestly-expressive space of nonlinear functions for high-information ones can be intractable. We describe an approach to feature construction where task-relevant discriminative features are automatically constructed, guided by an explanation-based interaction of training examples and prior domain knowledge. We show that in the challenging task of distinguishing handwritten Chinese characters, our automatic feature-construction approach performs particularly well on the most difficult and complex character pairs.