Neural Maps and Learning Vector Quantization - Theory and Applications

نویسندگان

  • Frank-Michael Schleif
  • Thomas Villmann
چکیده

Neural maps and Learning Vector Quantizer are fundamental paradigms in neural vector quantization based on Hebbian learning. The beginning of this field dates back over twenty years with strong progress in theory and outstanding applications. Their success lies in its robustness and simplicity in application whereas the mathematics beyond is rather difficult. We provide an overview on recent achievements and current trends of ongoing research.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

INTEGRATED 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 کامل

NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...

متن کامل

Air Quality Modelling by Kohonen’s Self-organizing Feature Maps and LVQ Neural Networks

The paper presents a design of parameters for air quality modelling and the classification of districts into classes according to their pollution. Further, it presents a model design, data pre-processing, the designs of various structures of Kohonen’s Self-organizing Feature Maps (unsupervised methods), the clustering by K-means algorithm and the classification by Learning Vector Quantization n...

متن کامل

Understanding Kohonen Networks

Kohonen neural nets are some kind of competitive nets. The most commonly known variants are the Self-Organizing Maps (SOMs) and the Learning Vector Quantization (LVQ). The former model uses an unsupervized learning, the latter is an e cient classi er. This paper tries to give, in simple words, a clear idea about the basis of competitive neural nets and competitive learning emphasizing on the SO...

متن کامل

Hierarchical overlapped SOM's for pattern classification

We develop a multilayer overlapped self-organizing maps (SOM's) with limited structure adaptation capabilities, and associated learning scheme for labeled pattern classification applications. The learning algorithm consists of the standard unsupervised SOM learning of synaptic weights as well as the supervised learning vector quantization (LVQ) 2 learning. As higher layer SOM's overlap, the fin...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2009