Models of incremental concept formation
Artificial Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
An incremental probabilistic neural network for regression and reinforcement learning tasks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Concept formation using incremental Gaussian mixture models
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Incremental feature-based mapping from sonar data using Gaussian mixture models
Proceedings of the 2011 ACM Symposium on Applied Computing
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This paper presents a new algorithm for unsupervised incremental learning based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), creates and continually adjusts a Gaussian Mixture Model consistent to all sequentially presented data. IGMM is particularly useful for on-line incremental clustering of data streams, as encountered in the domain of mobile robotics and animats. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. We present some preliminary results obtained using synthetic data and also consider practical issues as convergence properties discuss future developments.