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Fragmentization of Distance Measure for pattern Generation by a Self-Organizing Map

Fragmentization of Distance Measure for Pattern Generation b...

6th international conference on soft computing and Intelligent Systems (SCIS) / 13th international Symposium on Advanced Intelligence Systems (ISIS)

作者： Wakuya, Hiroshi Takahama, Eishi Itoh, Hideaki Fukumoto, Hisao Furukawa, Tatsuya Saga Univ Grad Sch Sci & Engn Honjo Saga 8408502 Japan

ISBN: (纸本)9781467327435;9781467327428

A self-organizing map (SOM) can be seen as an analytical tool to discover some underlying rules in the given data set. Based on such distinctive nature called topology-preserving projection, a new method for generating intermediate patterns was proposed. According to the results of preceding studies, most developed patterns are notmorphing but dissolve. Then, in order to overcome this problem, a fragmentized distance measure is introduced in this paper. As a result of computer simulations, it is confirmed that some asymmetrical patterns are developed even though only symmetrical ones are used for training. This fact reminds us that the distance measure is quite essential, because a feature map is developed through training based on the distance measure.

关键词： distance measurement pattern generation self-organising feature maps feature map

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A spiking neural network that extracts frequent symbol patterns from time-series data with different symbol appearance intervals 12

A spiking neural network that extracts frequent symbol patte...

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Joint 12th international conference on soft computing and Intelligent Systems / 23rd international Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Morita, Kenta Morita, Naoki Suzuka Univ Med Sci Fac Med Engn Tsu Mie Japan Tokai Univ Sch Informat Telecommun Engn Tokyo Japan

ISBN: (纸本)9781665499248

In this research, we aim to extract frequent symbol patterns from symbol sequences. In the target time series data, the appearance timing of symbols is different. The purpose is to absorb the deviation of the appearance timing and extract frequent symbol patterns. This paper proposes a spiking neural network that extracts frequent symbol patterns. The structure of the proposed network grows automatically each time a symbol is given. By learning the network, the output unit will only fire when given a frequent symbol pattern, and the network will extract the frequent symbol patterns. As a result of a simple experiment, it was confirmed that the proposed network can extract frequent symbol patterns from the target time series data.

关键词： Spiking neural network Time-series information processing pattern mining

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Event recognition hardware based on neuron MOS soft-computing circuits

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COMPUTERS & ELECTRICAL ENGINEERING 1997年第6期23卷 415-429页

作者： Shibata, T Yamashita, Y Konda, M Morimoto, T Ohmi, T Univ Tokyo Dept Informat & Commun Engn Bunkyo Ku Tokyo 113 Japan Tohoku Univ Dept Elect Engn Aoba Ku Sendai Miyagi 98077 Japan

Neuron MOS transistor (neuMOS or vMOS) mimicking the fundamental behavior of neurons at a primitive device level allows the implementation of intelligent functions directly on the integrated circuit hardware. Based on the vMOS technology a real-time event recognition system has been developed. A neuron MOS association processor searches for the event in the past memory having the maximum similarity to the current event presented to the system. This is based on Manhattan-distance calculation and the minimum distance search by a winner-take-all (WTA) circuitry. The computation is carried out directly on the hardware in a fully parallel architecture. A unique floating-gate analog EEPROM technology has been developed to build a vast memory system storing past events in multivalued vectors. Test circuits of key subsystems were fabricated by a double-polysilicon CMOS process and their operation was verified by measurements as well as by simulation. The vMOS circuit operation is characterized by analog computation directly conducted on an electrically floating node which is immediately followed by the thresholding action of a transistor to yield a binary decision. vMOS circuits would provide an opportunity for a very flexible soft computing scheme, while preserving the rigorous nature of digital processing. (C) 1998 Elsevier Science Ltd. All rights reserved.

关键词： neuron MOS transistor winner-take-all associative memory EEPROM vector quantization CMOS recognition Manhattan distance

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GA-Based Attempts to Improve the recognition Rate and Generalization Capacity of the Nonlinear soft Margin Support Vector Machines 18

GA-Based Attempts to Improve the Recognition Rate and Genera...

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18th international conference on System Theory, Control and computing (ICSTCC)

作者： State, Luminita Cocianu, Catalina Mircea, Marinela Univ Pitesti Dept Math & Informat Pitesti Romania Bucharest Univ Econ Studies Dept Informat & Cybernet Bucharest Romania

ISBN: (纸本)9781479946013

The aim of the paper is to report a new method based on genetic computation of designing a nonlinear soft margin SVM yielding to significant improvements in discriminating between two classes. The design of the SVM is performed in a supervised way, in general the samples coming from the classes being nonlinearly separable. The experimental analysis was performed on artificially generated data as well as on Ripley and MONK's datasets reported in the fourth section of the paper. The tests proved real improvements of both the recognition rate and generalization capacities without significantly increasing the computational complexity.

关键词： machine learning non-linear support vector machines soft margin SVM genetic algorithm kernel functions classifier design and evaluation model-free learning data driven control of parameters

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soft Margin SVM Modeling for Handling Imbalanced Human Activity Datasets in Multiple Homes

Soft Margin SVM Modeling for Handling Imbalanced Human Activ...

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international conference on Multimedia computing and Systems (ICMCS)

作者： Abidine, M'hamed Bilal Yala, Nawel Fergani, Belkacem Clavier, Laurent USTHB Fac Elect & Comp Sci Speech Commun & Signal Proc Lab Algiers Algeria Telecom LILLE Inst Mines Telecom IRCICA IEMNCNRSUMR 8520 Lille France

ISBN: (纸本)9781479938247

Activity recognition datasets are generally imbalanced, meaning certain activities occur more frequently than others. Not incorporating this class imbalance results in an evaluation that may lead to disastrous consequences for elderly persons. In this work, we evaluate various types of resampling methods: at algorithmic level using CS-SVM and at data level using SMOTE-CSVM and OS-CSVM combined with the discriminative classifier named soft-Margin Support Vector Machines (CSVM) in order to handle imbalanced data problem. We conduct several experiments using three real world activity recognition datasets and show that the SMOTE-CSVM and OS-CSVM are able to surpass CRF, CSVM and CS-SVM. OS-CSVM is slightly better than SMOTE-CSVM for classifying the activities using binary and ubiquitous sensors.

关键词： Activity recognition SVM Cost Sensitive Learning Machine Learning Imbalanced Data

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Discovering pattern of Onomatopoeia Used in Food Reviews

Discovering Pattern of Onomatopoeia Used in Food Reviews

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6th international conference on soft computing and Intelligent Systems (SCIS) / 13th international Symposium on Advanced Intelligence Systems (ISIS)

作者： Kato, Ayumi Fukazawa, Yusuke Mori, Taketoshi Univ Tokyo Grad Sch Interdisciplinary Informat Studies Tokyo Japan Univ Tokyo Sch Engn Tokyo Japan Univ Tokyo Grad Sch Med Tokyo Japan

ISBN: (纸本)9781467327435;9781467327428

In this paper, we investigated onomatopoeia usage pattern in food reviews by proposing LDA (Latent Dirichlet Allocation) based onomatopoeia usage pattern analysis model. We collected total 685 numbers of onomatopoeias which are distributed to 208 food categories from 3,581,808 food reviews of Japanese food review site Tabelog. From the experimental result, we found several patterns how the onomatopoeias are chosen. The onomatopoeia is chosen based on user's interest on the combination of {location of food, material of the food, cooking method} and {the texture of food, sound when eating, and looks of food people's status when eating the food}. In addition, we investigate how the precision of the clustering result changes depending on the N (number of onomatopoeia of each food categories). We found that the results of N=30 is better than one of N=100 as large number of onomatopoeia for each food categories like 100 is likely to include onomatopoeias that are irrelevant to food.

关键词： component onomatopoeia food review LDA term weighting new word discovery

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Container-Code pattern recognition Based on Attribute Grid computing

Container-Code Pattern Recognition Based on Attribute Grid C...

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IEEE international conference on Granular computing (GrC)

作者： Chen, Liang-dong Zeng, Wei-ming Wang, Ni-zhuan Shanghai Maritime Univ Digital Image & Intelligent Computat Lab Shanghai Peoples R China

ISBN: (纸本)9781479912827

A new container-code pattern recognition algorithm based on attribute grid computing is presented in this paper. The algorithm takes advantage of attribute grid computing, which is a new kind of calculator based on qualitative mapping. In this paper, character feature points are firstly modeled by qualitative criterion attribute grid computing. Then characteristics of each attribute are extracted and the corresponding attribute feature vector is established. Thus, the attribute feature vector can be used to train the model for each container-code character and finally to recognize the characters. By the attribute grid computing, our preliminary experimental results demonstrate an average recognition rate over 97% on hundreds of container-code characters. The results also demonstrate the feasibility of this method.

关键词： container-code pattern recognition qualitative mapping qualitative criterion attribute grid computing

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Approximate nearest neighbor search using a single space-filling curve and multiple representations of the data points

Approximate nearest neighbor search using a single space-fil...

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18th international conference on pattern recognition (ICPR 2006)

作者： Mainar-Ruiz, Gloria Perez-Cortes, Juan-Carlos Univ Politecn Valencia Inst Technol Informat Camino Vera S-N E-46071 Valencia Spain

ISBN: (纸本)0769525210

In this work, a fast approximate nearest neighbour search algorithm using single Space-filling Curve (SPFC) Mapping and a set of synthetic prototype representations is presented. The results are comparable to a multiple-spacefilling scheme, but achieving a much faster execution time, since computing multiple transformations and SPFC Mapping's is avoided, at the expense of having a more densely populated one-dimensional representation of the data-set. The advantages and limitations of the model are discussed, and an experimental evaluation with synthetic data and with a large, real high-dimensional optical character recognition data-set is presented.

关键词： pattern recognition

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An Approach of soft computing Applications in Clinical Neurology

An Approach of Soft Computing Applications in Clinical Neuro...

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6th international conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Simic, Dragan Simic, Svetlana Tanackov, Ilija Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Univ Novi Sad Fac Med Novi Sad 21000 Serbia

ISBN: (纸本)9783642212215

This paper briefly introduces various soft computing techniques and presents miscellaneous applications in clinical neurology domain. The aim is to present the large possibilities of applying soft computing to neurology related problems. Recently published data about use of soft computing in neurology are observed from the literature, surveyed and reviewed. This study detects which methodology or methodologies of soft computing are frequently used together to solve the specific problems of medicine. Recent developments in medicine show that diagnostic expert systems can help physicians make a definitive diagnosis. Automated diagnostic systems are important applications of pattern recognition, aiming at assisting physicians in making diagnostics decisions. soft computing models have been researched and implemented in neurology for a very long time. This paper presents applications of soft computing models of the cutting edge researches in neurology domain.

关键词： soft computing neurology neural networks fuzzy logic

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On classifier domains of competence

On classifier domains of competence

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17th international conference on pattern recognition (ICPR)

作者： Mansilla, EB Ho, TK Computer Engineering Department Ramon Llull University Spain Computing Sciences Research Center Bell Labs. Lucent Technologies United States

ISBN: (纸本)0769521282

We study the domain of dominant competence of six popular classifiers in a space of data complexity measurements. We observe that the simplest classifiers, nearest neighbor and linear classifier have extreme behavior of being the best for the easiest and the most difficult problems respectively, while the sophisticated ensemble classifiers tend to be robust for wider types of problems and are largely equivalent in performance. We characterize such behavior in detail using the data complexity metrics, and discuss how such a study can be matured for providing practical guidelines in classifier selection.

关键词： pattern recognition

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