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 generatin...
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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.
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 appearan...
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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.
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...
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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 softcomputing scheme, while preserving the rigorous nature of digital processing. (C) 1998 Elsevier Science Ltd. All rights reserved.
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...
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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.
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 consequ...
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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.
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...
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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.
A new container-code patternrecognition 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 qu...
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ISBN:
(纸本)9781479912827
A new container-code patternrecognition 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.
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 multip...
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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.
This paper briefly introduces various softcomputing techniques and presents miscellaneous applications in clinical neurology domain. The aim is to present the large possibilities of applying softcomputing to neurolo...
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ISBN:
(纸本)9783642212215
This paper briefly introduces various softcomputing techniques and presents miscellaneous applications in clinical neurology domain. The aim is to present the large possibilities of applying softcomputing to neurology related problems. Recently published data about use of softcomputing in neurology are observed from the literature, surveyed and reviewed. This study detects which methodology or methodologies of softcomputing 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 patternrecognition, aiming at assisting physicians in making diagnostics decisions. softcomputing models have been researched and implemented in neurology for a very long time. This paper presents applications of softcomputing models of the cutting edge researches in neurology domain.
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 ...
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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.
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