Entropy and information theory
Entropy and information theory
Elements of information theory
Elements of information theory
The nature of statistical learning theory
The nature of statistical learning theory
Divergence Based Feature Selection for Multimodal Class Densities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Information-Theoretic Bounds on Target Recognition Performance Based on Degraded Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Discriminative wavelet packet filter bank selection for pattern recognition
IEEE Transactions on Signal Processing
An Introduction to Statistical Signal Processing
An Introduction to Statistical Signal Processing
Tree pruning with subadditive penalties
IEEE Transactions on Signal Processing
Minimum probability of error image retrieval
IEEE Transactions on Signal Processing
Upper Bound Kullback–Leibler Divergence for Transient Hidden Markov Models
IEEE Transactions on Signal Processing
Recursive partitioning to reduce distortion
IEEE Transactions on Information Theory
Optimal pruning with applications to tree-structured source coding and modeling
IEEE Transactions on Information Theory
Quantized overcomplete expansions in IRN: analysis, synthesis, and algorithms
IEEE Transactions on Information Theory
Thresholding method for dimensionality reduction in recognition systems
IEEE Transactions on Information Theory
Analysis of a complexity-based pruning scheme for classification trees
IEEE Transactions on Information Theory
Minimax-optimal classification with dyadic decision trees
IEEE Transactions on Information Theory
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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This work presents new results in the context of minimum probability of error signal representation (MPE-SR) within the Bayes decision framework. These results justify addressing the MPE-SR criterion as a complexity-regularized optimization problem, demonstrating the empirically well understood trade-off between signal representation quality and learning complexity. Contributions are presented in three folds. First, the stipulation of conditions that guarantee a formal tradeoff between approximation and estimation errors under sequence of embedded transformations are provided. Second, the use of this tradeoff to formulate the MPE-SR as a complexity regularized optimization problem, and an approach to address this oracle criterion in practice is given. Finally, formal connections are provided between the MPE-SR criterion and two emblematic feature transformation techniques used in pattern recognition: the optimal quantization problem of classification trees (CART tree pruning algorithms), and some versions of Fisher linear discriminant analysis (LDA).