Elements of information theory
Elements of information theory
ACM Computing Surveys (CSUR)
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
LTF-C: architecture, training algorithm and applications of new neural classifier
Fundamenta Informaticae
Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science)
Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science)
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
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New algorithm for partitional data clustering is presented, Neural Society for Clustering (NSC). Its creation was inspired by hierarchical image understanding, which requires unsupervised training to build the hierarchy of visual features. Existing clustering algorithms are not well-suited for this task, since they usually split natural groups of patterns into several parts (like k-means) or give crisp clustering. Neurons comprising NSC may be viewed as a society of autonomous individuals, proceeding along the same simple algorithm, based on four principles: of locality, greediness, balance and competition. The same principles govern large groups of entities in economy, sociology, biology and physics. Advantages of NSC are demonstrated in experiment with visual data. The paper presents also a new method for objective and quantitative comparison of clustering algorithms, based on the notions of entropy and mutual information.