Results 251 to 260 of about 57,535 (295)

Hybrid learning vector quantization

Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), 2005
In this paper, a hybrid learning vector quantization algorithm is proposed. It modifies both the position of representative points and normalization parameters. Some of the experiments are operated on the synthetic and real data. The results show that the proposed hybrid learning vector quantization algorithm is applicable.
Sheng-Lin Chou, Yuan-Cheng Lai, Shiaw-Shian Yu   +2 more
openaire   +2 more sources

Learning Vector Quantization [PDF]

, 1995
Closely related to VQ and SOM is Learning Vector Quantization (LVQ). This name signifies a class of related algorithms, such as LVQ1, LVQ2, LVQ3, and OLVQ1. While VQ and the basic SOM are unsupervised clustering and learning methods, LVQ describes supervised learning.
openaire   +1 more source

Median learning vector quantizer

SPIE Proceedings, 1994
In this paper we propose a novel class of learning vector quantizers (LVQ) based on multivariate data ordering. Linear LVQ is not the optimal estimator for non-Gaussian multivariate data distributions. Furthermore, it is not robust either in the case of outliers or in the case of erroneous decisions. The novel LVQs use multivariate ordering in order to
Ioannis Pitas, P. Kiniklis
openaire   +2 more sources

CONSTRAINED LEARNING VECTOR QUANTIZATION

International Journal of Neural Systems, 1994
Kohonen’s learning vector quantization (LVQ) is an efficient neural network based technique for pattern recognition. The performance of the method depends on proper selection of the learning parameters. Over-training may cause a degradation in recognition rate of the final classifier. In this paper we introduce constrained learning vector quantization
openaire   +3 more sources

Regression Learning Vector Quantization

2009 Ninth IEEE International Conference on Data Mining, 2009
Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used because of their intuitively clear learning process and ease of implementation. In this paper we propose an extension of the LVQ algorithm to regression.
Slobodan Vucetic, Mihajlo Grbovic
openaire   +1 more source

Learning Vector Quantization Networks

Substance Use & Misuse, 1998
(1998). Learning Vector Quantization Networks. Substance Use & Misuse: Vol. 33, No. 2, pp. 271-282.
openaire   +3 more sources

Alternative learning vector quantization

Pattern Recognition, 2006
In this paper, we discuss the influence of feature vectors contributions at each learning time t on a sequential-type competitive learning algorithm. We then give a learning rate annealing schedule to improve the unsupervised learning vector quantization (ULVQ) algorithm which uses the winner-take-all competitive learning principle in the self ...
Kuo-Lung Wu, Miin-Shen Yang
openaire   +2 more sources

Learning vector quantization for (dis-)similarities

Neurocomputing, 2014
Prototype-based methods often display very intuitive classification and learning rules. However, popular prototype based classifiers such as learning vector quantization (LVQ) are restricted to vectorial data only. In this contribution, we discuss techniques how to extend LVQ algorithms to more general data characterized by pairwise similarities or ...
Hammer, Barbara, Hofmann, Daniela, Schleif, Frank-Michael, Zhu, Xibin   +3 more
openaire   +3 more sources

Fuzzy algorithms for learning vector quantization

IEEE Transactions on Neural Networks, 1996
This paper presents the development of fuzzy algorithms for learning vector quantization (FALVQ). These algorithms are derived by minimizing the weighted sum of the squared Euclidean distances between an input vector, which represents a feature vector, and the weight vectors of a competitive learning vector quantization (LVQ) network, which represent ...
Nicolaos B. Karayiannis, Pin-I Pai
openaire   +3 more sources

Relational Extensions of Learning Vector Quantization

2011
Prototype-based models offer an intuitive interface to given data sets by means of an inspection of the model prototypes. Supervised classification can be achieved by popular techniques such as learning vector quantization (LVQ) and extensions derived from cost functions such as generalized LVQ (GLVQ) and robust soft LVQ (RSLVQ). These methods, however,
Hammer, Barbara, Schleif, Frank-Michael, Zhu, Xibin, Lu, Bao-Liang, Zhang, Liqing, Kwok, James   +5 more
openaire   +2 more sources