C4.5: programs for machine learning
C4.5: programs for machine learning
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Support vector machines applied to face recognition
Proceedings of the 1998 conference on Advances in neural information processing systems II
Machine Learning
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, nonlinear features are introduced into a support vector machine (SVM) through a nonlinear kernel function. One disadvantage of such an approach is that the feature space induced by a kernel function is usually of high dimension and therefore will substantially increase the chance of over-fitting the training data. Another disadvantage is that nonlinear features are induced implicitly and therefore are difficult for people to understand which induced features are critical to the classification performance. In this paper, we propose a boosting-style algorithm that can explicitly induces important nonlinear features for SVMs. We present empirical studies with discussion to show that this approach is effective in improving classification accuracy for SVMs. The comparison with an SVM model using nonlinear kernels also indicates that this approach is effective and robust, particularly when the number of training data is small.