AIP Conference Proceedings 151 on Neural Networks for Computing
A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Fuzzy prediction and filtering in impulsive noise
Fuzzy Sets and Systems - Special issue on fuzzy signal processing
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
A clustering algorithm based on graph connectivity
Information Processing Letters
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
A Hypergraph Based Clustering Algorithm for Spatial Data Sets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Stochastic resonance in noisy threshold neurons
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Noise enhanced nonparametric detection
IEEE Transactions on Information Theory
An information-theoretic analysis of hard and soft assignment methods for clustering
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Noise-enhanced performance for an optimal Bayesian estimator
IEEE Transactions on Signal Processing
Noise Benefits in Quantizer-Array Correlation Detection and Watermark Decoding
IEEE Transactions on Signal Processing
Noise-Enhanced Detection of Subthreshold Signals With Carbon Nanotubes
IEEE Transactions on Nanotechnology
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
Differential competitive learning for centroid estimation and phoneme recognition
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Stochastic competitive learning
IEEE Transactions on Neural Networks
Stochastic Resonance in Continuous and Spiking Neuron Models With Levy Noise
IEEE Transactions on Neural Networks
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks
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Noise can provably speed up convergence in many centroid-based clustering algorithms. This includes the popular k-means clustering algorithm. The clustering noise benefit follows from the general noise benefit for the expectation-maximization algorithm because many clustering algorithms are special cases of the expectation-maximization algorithm. Simulations show that noise also speeds up convergence in stochastic unsupervised competitive learning, supervised competitive learning, and differential competitive learning.