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1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Nayef, Baher H. Hussain, Rizuana Iqbal Sahran, Shahnorbanun Abdullah, Siti Norul Huda Sheikh Univ Kebangsaan Malaysia FTSM Ctr Artificial Intelligence Technol Pattern Recognit Res Grp Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia UKM Med Ctr Dept Radiol Kuala Lumpur 56000 Malaysia

ISBN: (纸本)9781467328210;9781467328203

The performance accuracy of the Artificial Neural Network (ANN) is highly dependent on the class distribution. Data multi-randomization before classification is proposed in this paper in order to obtain a proper classification model, which guaranties well performance of the classifiers. Multi randomization aims to allocate the best class distribution by re-ordering the input dataset randomly. In this paper, learning vector quantization (LVQ) which is a supervised ANN, Multilayer perceptron (MLP), unsupervised Self organizing Map (SOM) and Radial Base Function (RBF) are used to classify multi randomized brain Magnetic Resonance Imaging (MRI) dataset. The proposed method showed significant improvement in the stability of the classifiers.

关键词： Artificial Neural Network data multi-resampling learning vector quantization Magnetic Resonance Imaging

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A BATCH learning vector quantization ALGORITHM FOR CATEGORICAL DATA

A BATCH LEARNING VECTOR QUANTIZATION ALGORITHM FOR CATEGORIC...

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1st International Conference on Agents and Artificial Intelligence

作者： Chen, Ning Marques, Nuno C. Inst Politecn Porto Inst Super Engn Porto GECAD Rua Dr Antonio Bernardino Almeida 431 P-4200072 Oporto Portugal FCT UNL CENTRIA Departamento Informat Almada Portugal

ISBN: (纸本)9789898111661

learning vector quantization (LVQ) is a supervised learning algorithm for data classification. Since LVQ is based on prototype vectors, it is a neural network approach particularly applicable in non-linear separation problems. Existing LVQ algorithms are mostly focused on numerical data. This paper presents a batch type LVQ algorithm used for mixed numerical and categorical data. Experiments on various data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to deal with categorical data.

关键词： learning vector quantization Self-organizing map Categorical Batch SOM

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The Classification of Fetus Gender on Ultrasound Images Using learning vector quantization (LVQ)

The Classification of Fetus Gender on Ultrasound Images Usin...

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2014 Makassar International Conference on Electrical Engineering and Informatics (MICEEI)

作者： Maysanjaya, I. Md Dendi Nugroho, Hanung Adi Setiawan, Noor Akhmad Univ Gadjah Mada Fac Engn Dept Elect Engn & Informat Technol Jl Grafika 2Kampus UGM Yogyakarta 55281 Indonesia

ISBN: (纸本)9781479967261

One example of the implementations of digital image processing in biomedical field is to identify the gender of the fetus on the ultrasound image. To identify the gender of the fetus, a fetal must attain the age of at least 5 months of pregnancy. Before the process of identification, there are three steps that must be done, i.e. image preprocessing, image segmentation, and feature extraction (shape description). Having obtained the value of the feature extraction stage, the next step is the classification by utilizing one of the artificial neural network (ANN) methods, namely the learning vector quantization (LVQ). Prior to the LVQ process, the training datasets process is conducted beforehand with 3 iterations using the learning rate of 0.05 and the learning rate reduction of 0.02 per iteration. Then the training process is followed by a classification stage. The obtained test results show that the LVQ classification gives poor results. The less optimal results are generated due to the quality of the dataset used. The quality of this dataset is affected by the results of the digitization process, the stage of preprocessing, segmentation, and feature extraction.

关键词： ultrasound image fetus gender learning vector quantization

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Extending learning vector quantization for Classifying Data with Categorical Values

Extending Learning Vector Quantization for Classifying Data ...

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1st International Conference on Agents and Artificial Intelligence

作者： Chen, Ning Marques, Nuno C. Inst Politecn Porto GECAD Inst Super Engn Porto Rua Dr Antonio Bernardino de Almeida 431 P-4200072 Oporto Portugal Univ Nova Lisboa Fac Ciencias & Tecnol CENTRIA Dept Informat P-2829 Lisbon Portugal

ISBN: (纸本)9783642118180

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 for data in categorical nonvector spaces. Experiments on UCI data sets demonstrate the proposed algorithm is effective to improve the capability of standard LVQ to handle data with categorical values.

关键词： learning vector quantization Self-organizing map Categorical value Batch learning

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Training a learning vector quantization network for biomedical classification

Training a Learning Vector Quantization network for biomedic...

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International Joint Conference on Neural Networks (IJCNN 01)

作者： Anagnostopoulos, C Anagnostopoulos, J Vergados, DD Kayafas, E Loumos, V Theodoropoulos, G Natl Tech Univ Athens Dept Elect & Comp Engn GR-15773 Athens Greece

ISBN: (纸本)0780370449

A competitive learning vector quantization (LVQ) Artificial Neural Network (ANN) was trained to identify third stage parasitic strongyle larvae from domestic animals on the basis of quantitative data obtained from processed digital images of larvae. For this reason various novel quantitative features obtained from processed digital images of larvae were tested whether they are variant or invariant to the shape taken by the motile larvae during image recording. A total of 255 images of 57 individual larvae in various shapes belonging to 5 genera were recorded. Following image processing 16 novel features were measured of which 7 were selected as invariant to larva shape. By trial and error two of those novel features "area" and "perimeter" along with the quantitative features used in conventional identification, "overall body length", "width" and "tail of sheath" were used as an effective training data set for the ANN. This ANN coupled with an image analysis facility and a knowledge relational database became the basis for developing a computer-based larva identification system whose overall identification performance was 91.9 %. The advantages of this system are its speed and objectivity. The objectivity of the system is based on the fact that it is not subject to inter- and intra-observer variability arising from the user's profile of competency in interpreting subjective and non-quantifiable descriptions. The limitations of the system are that it can not handle raw images but only data extracted from images, its performance depends on the reliability of the input vectors used as training data for the ANN and its use is restricted only in well equipped laboratories due to its requirement for expensive instrumentation.

关键词： image processing neural networks learning vector quantization parasite identification

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A study on Visual Abstraction for Reinforcement learning Problem Using learning vector quantization

A study on Visual Abstraction for Reinforcement Learning Pro...

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SICE Annual Conference

作者： Faudzi, Ahmad Afif Mohd Takano, Hirotaka Murata, Junichi Kyushu Univ Dept Elect & Elect Engn Fukuoka 812 Japan Univ Malaysia Pahang Dept Elect & Elect Engn Gambang Malaysia Kyushu Univ Dept Elect Engn Fukuoka 812 Japan

ISBN: (纸本)9784907764432

When applying the learning systems to real-world problems, which have a lot of unknown or uncertain things, there are some issues that need to be solved. One of them is the abstraction ability. In reinforcement learning, to complete each task, the agent will learn to find the best policy. Nevertheless, if a different task is given, we cannot know for sure whether the acquired policy is still valid or not. However, if we can make an abstraction by extract some rules from the policy, it will be easier to understand and possible to apply the policy to different tasks. In this paper, we apply the abstraction at a perceptual level. In the first phase, an action policy is learned using Q-learning, and in the second phase, learning vector quantization is used to extract information out of the learned policy. In this paper, it is verified that by applying the proposed abstraction method, a more useful and simpler representation of the learned policy can be achieved.

关键词： Abstraction learning vector quantization Reinforcement learning

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learning vector quantization and Permutation Entropy to Analyse Epileptic Electroencephalography

Learning Vector Quantization and Permutation Entropy to Anal...

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International Joint Conference on Neural Networks

作者： Nadia Mammone Troels Wesenberg Kjr Jonas Duun-Henriksen Maurizio Campolo Fabio La Foresta Francesco C. Morabito IRCCS Centro Neurolesi Bonino-Pulejo Via Palermo c/da Casazza SS. 113 98124 Messina Italy Neurophysiology Center Department of Neurology Roskilde University Hospital Koegevej 7-13 DK-4000 Roskilde Denmark HypoSafe A/S Diplomvej 381 2800 Kgs. Lyngby Denmark DICEAM Department Mediterranean University of Reggio Calabria Via Graziella Feo di Vito 89060 Reggio Calabria Italy

ISBN: (纸本)9781479919611

In this paper, we address the issue of dealing with huge amounts of data from recordings of an Electroencephalogram (EEG) in epileptic patients. In particular, the attention is focused on the development of tools to support the neurophysi-ologists in the time consuming and challenging task of reviewing the EEG to identify critical events that are worth of inspection for diagnostic purposes. A novel methodology is proposed for the automatic estimation of descriptors of EEG complexity and the subsequent classification of critical events. Based on the estimation of Permutation Entropy (PE) profiles from the EEG traces, the methodology relies on learning vector quantization (LVQ) to cluster the electrodes in a competitive way according to their PE levels and to classify the cerebral state accordingly. An absence seizure EEG of 15.5 minutes was processed and a 93.94% sensitivity together with a 100% specificity were obtained.

关键词： quantization Permutation Entropy Permutation learning vector quantization Electroencephalography entropy quantization vector quantization Epilepsy

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Analysis of Robust Soft learning vector quantization and an application to Facial Expression Recognition — Extended Abstract —

Analysis of Robust Soft Learning Vector Quantization and an ...

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Similarity-Based learning on Structures 2009

作者： de Vries, Gert-Jan Biehl, Michael Philips Research Europe User Experiences High Tech Campus 34 EindhovenNL-5656 AE Netherlands University of Groningen Inst. of Mathematics and Computing Science P.O. Box 407 Groningen9700 AK Netherlands

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Semantic classification of rural and urban images using learning vector quantization

Semantic classification of rural and urban images using lear...

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作者： Prakash Thulasiraman Louisiana State University

学位级别：硕士

One of the major hurdles in semantic image classification is that only low-level features can be reliably extracted from images as opposed to higher level features (objects present in the scene and their inter-relationships). The main challenge lies in grouping images into semantically meaningful categories based on the available low-level visual features of the images. It is important that we have a classification method that will handle a complex image dataset with not so well defined boundaries between clusters. learning vector quantization (LVQ) neural networks offer a great deal of robustness in clustering complex datasets. This study presents a semantic image classification using LVQ neural network that uses low level texture, shape, and color features that are extracted from images from rural and urban domains using the Box Counting Dimension method (Peitgen et al. 1992), Fast Fourier Transformation and HSV color space. The performance measures precision and recall were calculated while using various ranges of input parameters such as learning rate, iterations, number of hidden neurons for the LVQ network. The study also tested for the feature robustness for image object orientation (rotation and position) and image size. Our method was compared against the method given in Prabhakar et al, 2002. The precision and recall while using various combination of texture, shape, and color features for our method was between.68 and.88, and 0.64 and.90 respectively compared against the precision and recall (for our image data set) of 0.59 and.62 for the method given by Prabhakar et al., 2002.

关键词： Semantic classification feature invariance learning vector quantization LVQ HSV color space Box Counting texture Fourier descriptors

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Nearest neighbor and learning vector quantization classification for damage detection using time series analysis

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STRUCTURAL CONTROL & HEALTH MONITORING 2010年第6期17卷 614-631页

作者： de Lautour, Oliver R. Omenzetter, Piotr Univ Auckland Dept Civil & Environm Engn Auckland 1142 New Zealand

The application of time series analysis methods to structural health monitoring (SHM) is a relatively new but promising approach. This study focuses on the use of statistical pattern recognition techniques to classify damage based on analysis of the time series model coefficients. Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures;a three-storey laboratory bookshelf structure and the ASCE Phase II experimental SHM benchmark structure in undamaged and various damaged states. The coefficients of the AR models were used as damage sensitive features. Principal component analysis and Sammon mapping were used to firstly obtain two-dimensional projections for quick visualization of clusters among the AR coefficients corresponding to the various damage states, and later for dimensionality reduction of data for automatic damage classifications. Data reduction based on the selection of sensors and AR coefficients was also studied. Two supervised learning algorithms, nearest neighbor classification and learning vector quantization were applied in order to systematically classify damage into states. The results showed both classification techniques were able to successfully classify damage. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： structural health monitoring damage detection time series analysis autoregressive models nearest neighbor classification learning vector quantization

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