Entropy and information theory
Entropy and information theory
Elements of information theory
Elements of information theory
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Vector Quantization and Density Estimation
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Concept learning using complexity regularization
IEEE Transactions on Information Theory
On minimizing distortion and relative entropy
IEEE Transactions on Information Theory
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Classification of pulse waveforms using edit distance with real penalty
EURASIP Journal on Advances in Signal Processing
Pulse waveform classification using ERP-Based difference-weighted KNN classifier
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Multi-label weighted k-nearest neighbor classifier with adaptive weight estimation
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
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Nonparametric neighborhood methods for learning entail estimation of class conditional probabilities based on relative frequencies of samples that are "near-neighbors” of a test point. We propose and explore the behavior of a learning algorithm that uses linear interpolation and the principle of maximum entropy (LIME). We consider some theoretical properties of the LIME algorithm: LIME weights have exponential form; the estimates are consistent; and the estimates are robust to additive noise. In relation to bias reduction, we show that near-neighbors contain a test point in their convex hull asymptotically. The common linear interpolation solution used for regression on grids or look-up-tables is shown to solve a related maximum entropy problem. LIME simulation results support use of the method, and performance on a pipeline integrity classification problem demonstrates that the proposed algorithm has practical value.