# Some Competitive Learning Methods Contents 1 Introduction 3 2 Common Properties & Notational Conventions 4 3 Goals of Competitive Learning 7

@inproceedings{Fritzke1997SomeCL, title={Some Competitive Learning Methods Contents 1 Introduction 3 2 Common Properties \& Notational Conventions 4 3 Goals of Competitive Learning 7}, author={Bernd Fritzke}, year={1997} }

(Some additions and reenements are planned for this document so it will stay in the draft status still for a while.) Comments are welcome. Abstract This report has the purpose of describing several algorithms from the literature all related to competitive learning. A uniform terminology is used for all methods. Moreover, identical examples are provided to allow a qualitative comparisons of the methods. The on-line version 1 of this document contains hyperlinks to Java implementations of several… Expand

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#### 8 Citations

A Mixed Ensemble Approach for the Semi-supervised Problem

- Computer Science
- ICANN
- 2002

This approach consists of an ensemble unsupervised learning part where the labeled and unlabeled points are segmented into clusters and takes advantage of the a priori information of the labeled points to assign classes to clusters and proceed to predicting with the ensemble method new incoming ones. Expand

Architecture for graphical maps of Web contents

- 2004

Efficient visualization of large collection of documents seems a primary topic in intelligent navigation through Internet resources. The paper describes a set of tools explored in the course of… Expand

Multi-topographic neural network communication and generalization for multi-viewpoint analysis

- Computer Science
- Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.
- 2005

A new generic multitopographic neural network model whose main area of application is clustering and knowledge extraction tasks on documentary data is presented and it is shown how its generalization mechanism and its mechanism of communication between topographies can be exploited within the framework of the SOM and NG models. Expand

Clustering algorithms for scenario tree generation: Application to natural hydro inflows

- Mathematics, Computer Science
- Eur. J. Oper. Res.
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This article uses a procedure to create the scenario tree divided into two phases: the first one produces a tree that represents accurately the original probability distribution, and in the second phase that tree is reduced to make it tractable. Expand

A visual data-mining methodology for seismic-facies analysis : P s

- 2008

Seismic facies analysis aims to identify clusters groups of similar seismic trace shapes, where each cluster can be considered to represent variability in lithology, rock properties, and/or fluid… Expand

Advances in Radio Science Context-based user grouping for multicasting in heterogeneous radio networks

- 2011

Along with the rise of sophisticated smartphones and smart spaces, the availability of both static and dynamic context information has steadily been increasing in recent years. Due to the popularity… Expand

Operational Two-Stage Stratified Topographic Correction of Spaceborne Multispectral Imagery Employing an Automatic Spectral-Rule-Based Decision-Tree Preliminary Classifier

- Mathematics, Computer Science
- IEEE Transactions on Geoscience and Remote Sensing
- 2010

The novel operational two-stage SNLTOC system is presented and its capability of reducing within-stratum spectral variance while preserving pixel-based spectral patterns (shapes) is assessed quantitatively. Expand

Fuzzy voting in clustering

- Mathematics
- 1999

In this paper we present a fuzzy voting scheme for cluster algorithms. This fuzzy voting method allows us to combine several runs of cluster algorithms resulting in a common fuzzy partition. This… Expand

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