Proceedings ArticleDOI
Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map
J. Blackmore,Risto Miikkulainen +1 more
- pp 450-455
TLDR
In the proposed approach, nodes are added incrementally to a regular two-dimensional grid, which is drawable at all times, irrespective of the dimensionality of the input space, resulting in a map that explicitly represents the cluster structure of the high-dimensional input.Abstract:
Ordinary feature maps with fully connected, fixed grid topology cannot properly reflect the structure of clusters in the input space. Incremental feature map algorithms, where nodes and connections are added to or deleted from the map according to the input distribution can overcome this problem. Such algorithms have been limited to maps that can be drawn in 2-D only in the case of two-dimensional input space. In the proposed approach, nodes are added incrementally to a regular two-dimensional grid, which is drawable at all times, irrespective of the dimensionality of the input space. The process results in a map that explicitly represents the cluster structure of the high-dimensional input. >read more
Citations
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Journal ArticleDOI
Review: A review of novelty detection
TL;DR: This review aims to provide an updated and structured investigation of novelty detection research papers that have appeared in the machine learning literature during the last decade.
Journal ArticleDOI
Growing cell structures—a self-organizing network for unsupervised and supervised learning
TL;DR: A new self-organizing neural network model that has two variants that performs unsupervised learning and can be used for data visualization, clustering, and vector quantization is presented and results on the two-spirals benchmark and a vowel classification problem are presented that are better than any results previously published.
Journal ArticleDOI
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
TL;DR: The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data, and by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated.
Journal ArticleDOI
A self-organising network that grows when required
TL;DR: In this paper, the authors proposed a growing neural gas (GNG) algorithm, which can add nodes whenever the network in its current state does not sufficiently match the input, but stops growing once the network has matched the data.
Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997
TL;DR: A comprehensive list of papers that use the Self-Organizing Map algorithms, have bene ted from them, or contain analyses of them is collected and provided both a thematic and a keyword index to help find articles of interest.
References
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Book
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TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
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Self-organizing semantic maps
Helge Ritter,Teuvo Kohonen +1 more
TL;DR: Self-organized formation of topographic maps for abstract data, such as words, is demonstrated and it is argued that a similar process may be at work in the brain.
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Variants of self-organizing maps
TL;DR: Two innovations are discussed: dynamic weighting of the input signals at each input of each cell, which improves the ordering when very different input signals are used, and definition of neighborhoods in the learning algorithm by the minimum spanning tree, which provides a far better and faster approximation of prominently structured density functions.
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TL;DR: This chapter discusses the development and application of Parallel Computers, self-Organization and Learning in Neural Networks, and Selected Applications for Neural Networks.
Journal ArticleDOI
Natural Language Processing With Modular PDP Networks And Distributed Lexicon
TL;DR: An approach to connectionist natural language processing is proposed, which is based on hierarchically organized modular parallel distributed processing (PDP) networks and a central lexicon of distributed input/output representations.