LVQ, or Learning Vector Quantization, is a prototype-based supervised classification algorithm. A prototype is an original type form or instance of something serving as a typical example basis or standard for other things of the same category Supervised learning is a Machine learning technique for learning a function from training data Statistical classification is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred In Mathematics, Computing, Linguistics and related subjects an algorithm is a sequence of finite instructions often used for Calculation
LVQ can be understood as a special case of an artificial neural network, more precisely, it applies a winner-take-all Hebbian learning-based approach. An artificial neural network (ANN, often just called a "neural network" (NN is a Mathematical model or Computational model based on Biological neural Winner-take-all connotates also the principle of the Plurality voting system. Hebbian theory describes a basic mechanism for Synaptic plasticity wherein an increase in synaptic efficacy arises from the Presynaptic cell's repeated It is a precursor to Self-organizing maps (SOM) and related to Neural gas, and to the k-Nearest Neighbor algorithm (k-NN). A self-organizing map (SOM is a type of Artificial neural network that is trained using Unsupervised learning to produce a low-dimensional (typically two dimensional Neural Gas - a Biologically inspired adaptive Algorithm, coined by Martinetz and Schulten 1991 In Pattern recognition, the k -nearest neighbor algorithm ( k -NN is a method for classifying objects based on closest training examples in the LVQ was invented by Teuvo Kohonen. Teuvo Kohonen, Dr Ing (born July 11, 1934) is a Finnish academican and prominent researcher
The network has two layers: a layer of input neurons, and a layer of output neurons. The network is given by prototypes W=(w(i),. . . ,w(n)). It changes the weights of the network in order to classify the data correctly. For each data point, the prototype (neuron) that is closest to it is determined (called the winner neuron). The weights of the connections to this neuron are then adapted, i. e. made closer if it correcly classifies the data point or made less similar if it incorrectly classifies it.
An advantage of LVQ is that it creates prototypes that are easy to interpret for experts in the field.
LVQ can be a source of great help in classifying text documents.
References
- Self-Organizing Maps and Learning Vector Quantization for Feature Sequences, Somervuo and Kohonen. 2004 (pdf)
- Classification of Textual Documents using LVQ, Fahad and Sikander. 2007 (pdf)
See also
External links
- Introduction to the algorithm. Plus download of codes and programs implementing it (University of Magdeburg)
- LVQ for WEKA: Implementation of LVQ variants (LVQ1, OLVQ1, LVQ2. A self-organizing map (SOM is a type of Artificial neural network that is trained using Unsupervised learning to produce a low-dimensional (typically two dimensional 1, LVQ3, OLVQ3) for the WEKA Machine Learning Workbench.
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