Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of features extracted by ICA (ICA fe...
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Recently, Independent Component Analysis (ICA) has been applied to not only problems of blind signal separation, but also feature extraction of patterns. However, the effectiveness of features extracted by ICA (ICA features) has not been verified yet. As one of the reasons, it is considered that ICA features are obtained by increasing their independence rather than by increasing their class separability. Hence, we can expect that high-performance pattern features are obtained by introducing supervisor into conventional ICA algorithms such that the class separability of features is enhanced.. In this work, we propose SICA by maximizing Mahalanobis distance between classes. Moreover, we propose a new distance measure in which each ICA feature is weighted by the power of principal components consisting of the ICA feature. In the recognition experiments, we demonstrate that the better recognition accuracy for two data sets in UCI machinelearning Repository is attained when using features extracted by the proposed SICA.
A new direction in machinelearning area has emerged from Vapnik's theory in support vectors machine and its applications on patternrecognition. In this paper, we propose a new SVM kernel family (KMOD) with disti...
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ISBN:
(纸本)0769512631
A new direction in machinelearning area has emerged from Vapnik's theory in support vectors machine and its applications on patternrecognition. In this paper, we propose a new SVM kernel family (KMOD) with distinctive properties that allots, better discrimination in the feature space. the experiments that we carry out show its effectiveness on synthetic and large-scale data. We found KMOD behaving better than RBF and Exponential RBF kernels on the two-spiral problem. In addition, a digit recognition task was processed using the proposed kernel. the results show;at least, comparable performances to state of the art kernels.
this paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of "divide and conquer" principle and ensemble method. the learning framework co...
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ISBN:
(纸本)0769512631
this paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of "divide and conquer" principle and ensemble method. the learning framework consists of quantization layer and ensemble layer. After GLVQ and MLP are applied to the framework. the proposed method is tested on MNIST handwritten digit database. the obtained performance is very promising, an error rate with 0.99. which is comparable to that of LeNet5. one of the best classifiers on this database, Further, in contrast to LeNet7, our method is especially suitable for a large-scale real-world classification problem.
Me propose a geometric method for document image processing. this research focuses on document understanding and classification by applying the Winnow algorithm, an on-line machinelearning method this application sta...
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ISBN:
(纸本)0769512631
Me propose a geometric method for document image processing. this research focuses on document understanding and classification by applying the Winnow algorithm, an on-line machinelearning method this application stakes the document image processing more flexible with various kind of documents since the meaningful knowledge can be extracted from training examples and the model for document ti-pe can be updated when there is a new example. this research aims to anal v: a and classify scientific papers. We conduct the experiments on documents from the proceedings of various conferences to show the performance of the proposed method the experimental results are compared withthe WISDOM++ system and also show the advantages of using the on-line machinelearning method.
Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is indi...
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ISBN:
(纸本)0769512631
Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individualistic. Handwriting samples of one thousand five hundred individuals, representative of the US population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. these attributes, which are a subset of attributes used by expert document examiners, were used to quantitatively establish individuality by using machinelearning approaches. Using global attributes of hadwriting and very, few characters in the writing, the ability to determine the writer with a high degree of confidence was established. the work is a step towards providing scientific support for admitting handwriting evidence in court. the mathematical approach and the resulting software also have the promise of aiding the expert document examiner.
Document image understanding denotes the recognition of semantically relevant components in the layout extracted from a document image. this recognition process is based on some visual models, whose manual specificati...
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Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is indi...
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ISBN:
(纸本)0769512631
Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individualistic. Handwriting samples of 1,500 individuals, representative of the US population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features froth scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. these attributes, which are a subset of attributes used by expert document examiners. were used to quantitatively, establish individuality by using machinelearning approaches. Using global attributes of handwriting and yen, few characters in the writing, the ability to determine the writer with a high degree of confidence Was established. the work is a step towards providing scientific support for admitting handwriting evidence in court. the mathematical approach curd the resulting software also have the promise of aiding the expert document examiner.
In this paper a novel datamining technique – Clustering and Classification Algorithm-Supervised (CCA-S)1 is introduced. CCA-S supports incremental learning and non-hierarchical clustering, and is scalable for proces...
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the proceedings contain 101 papers. the special focus in this conference is on Bio-inspired Systems, Engineering, Methodology for Nets Design, Nets Simulation and Image Processing. the topics include: Design and codes...
ISBN:
(纸本)9783540422372
the proceedings contain 101 papers. the special focus in this conference is on Bio-inspired Systems, Engineering, Methodology for Nets Design, Nets Simulation and Image Processing. the topics include: Design and codesign of neuro-fuzzy hardware;parametric neurocontroller for positioning of an anthropomorfic finger based on an oponent driven-tendon transmission system;an integration principle for multimodal sensor data based on temporal coherence of self-organized patterns;a modular neural networks simulator for Beowulf clusters;repeated measures multiple comparison procedures applied to model selection in neural networks;designing and training arbitrary neural networks in java;optimal genetic representation of complete strictly-layered feed forward neural networks;assessing the noise immunity of radial basis function neural networks;analyzing Boltzmann machine parameters for fast convergence;a penalization criterion based on noise behaviour for model selection;dynamic topology networks for colour image compression;analysis on the viewpoint dependency in 3-d object recognition by support vector machines;a comparative study of two neural models for cloud screening of Iberian peninsula meteosat images;self-organizing map for hyperspectral image analysis;classification of the images of gene expression patterns using neural networks based on multi-valued neurons;automatic generation of digital filters by NN based learning;neural network based on multi-valued neurons;partial classification in speech recognition verification;a comparative study of ICA filter structures learnt from natural and urban images and an automatic system for the location of the optic nerve head from 2d images.
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