Dynamical analysis of LVQ type learning rules
نویسندگان
چکیده
Learning vector quantization (LVQ) constitutes a powerful and simple method for adaptive nearest prototype classification which has been introduced based on heuristics. Recently, a mathematical foundation by means of a cost function has been proposed which, as a limit case, yields a learning rule very similar to classical LVQ2.1 and also motivates a modification thereof which shows better stability. However, the exact dynamics as well as the generalization ability of the LVQ algorithms have not been investigated so far in general. Using concepts from statistical physics and the theory of on-line learning, we present a rigorous mathematical investigation of the dynamics of LVQ type classifiers in a prototypical scenario. Interestingly, one can observe significant differences of the algorithmic stability and generalization ability and quite unexpected behavior for these only slightly different variants of LVQ.
منابع مشابه
Extending Learning Vector Quantization for Classifying Data with Categorical Values
Learning vector quantization (LVQ) is a supervised neural network method applicable in non-linear separation problems and widely used for data classification. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for classifying data with categorical values. The batch learning rules make possible to construct the learning methodology f...
متن کاملINTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...
متن کاملAnalysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition
Learning Vector Quantization (LVQ) [1] is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) [2] is a promising one. Although LVQ methods have an intuitive design with clear updating rules, their dynamics are not yet well understood. In simulations within a controlled environment RSLVQ p...
متن کاملLVQ algorithm with instance weighting for generation of prototype-based rules
Crisp and fuzzy-logic rules are used for comprehensible representation of data, but rules based on similarity to prototypes are equally useful and much less known. Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with the Learning Vector Quantization (LVQ) algorithm being a prominent examp...
متن کاملImproving Accuracy of LVQ Algorithm by Instance Weighting
Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with Learning Vector Quantization (LVQ) algorithm being a prominent example. Accuracy of LVQ highly depends on proper initialization of prototypes and the optimization mechanism. Prototype initialization based on context dependent clustering...
متن کامل