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
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Sensory Flow Segmentation Using a Resource Allocating Vector Quantizer
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Toward a Theory of Embodied Statistical Learning
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
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
Incremental learning of multivariate Gaussian mixture models
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
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 incremental concept formation based on a Bayesian framework. The algorithm, called IGMM (for Incremental Gaussian Mixture Model), uses a probabilistic approach for modeling the environment, and so, it can rely on solid arguments to handle this issue. IGMM creates and continually adjusts a probabilistic model consistent to all sequentially presented data without storing or revisiting previous training data. IGMM is particularly useful for incremental clustering of data streams, as encountered in the domain of moving object trajectories and mobile robotics. It creates an incremental knowledge model of the domain consisting of primitive concepts involving all observed variables. Experiments with simulated data streams of sonar readings of a mobile robot shows that IGMM can efficiently segment trajectories detecting higher order concepts like "wall at right" and "curve at left".