Arbitrarily Tight Upper and Lower Bounds on the Bayesian Probability of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
The Hilbert Kernal regression estimate
Journal of Multivariate Analysis
Machine Learning
New multivariate product density estimators
Journal of Multivariate Analysis
A Tight Upper Bound on the Bayesian Probability of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
A generalized kernel approach to dissimilarity-based classification
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Efficient Margin Maximizing with Boosting
The Journal of Machine Learning Research
Margin Trees for High-dimensional Classification
The Journal of Machine Learning Research
Analytical Results on Style-Constrained Bayesian Classification of Pattern Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
On a New Class of Bounds on Bayes Risk in Multihypothesis Pattern Recognition
IEEE Transactions on Computers
Compression-Based Averaging of Selective Naive Bayes Classifiers
The Journal of Machine Learning Research
Large Margin Semi-supervised Learning
The Journal of Machine Learning Research
VC Theory of Large Margin Multi-Category Classifiers
The Journal of Machine Learning Research
Learning Similarity with Operator-valued Large-margin Classifiers
The Journal of Machine Learning Research
Latent-Space Variational Bayes
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Unreasonable Effectiveness of Data
IEEE Intelligent Systems
Latent classification models for binary data
Pattern Recognition
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Regularized margin-based conditional log-likelihood loss for prototype learning
Pattern Recognition
Large margin cost-sensitive learning of conditional random fields
Pattern Recognition
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Sensitivity Analysis in Bayesian Classification Models: Multiplicative Deviations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Concept learning using complexity regularization
IEEE Transactions on Information Theory
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Motivated by the potential field of static electricity, a binary potential function classifier views each training sample as an electrical charge, positive or negative according to its class label. The resulting potential field divides the feature space into two decision regions based on the polarity of the potential. In this paper, we revisit potential function classifiers in their original form and reveal their connections with other well-known results in the literature. We derive a bound on the generalization performance of multiclass potential function classifiers based on the observed margin distribution of the training data. A new model selection criterion using a normalized margin distribution is then proposed to learn ''good'' potential function classifiers in practice.