Two distinct principles of multi-modal kernel-based patternrecognition, kernel and classifier fusion, are demonstrated to share common underlying characteristics via the use of a novel kernel-based technique for comb...
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ISBN:
(纸本)9781424409723
Two distinct principles of multi-modal kernel-based patternrecognition, kernel and classifier fusion, are demonstrated to share common underlying characteristics via the use of a novel kernel-based technique for combining modalities under fully general conditions, namely, the neutral-point method. this method presents a conservative kernel-based strategy for dealing with missing and disjoint training data in independent measurement modalities that can be theoretically shown to default to the Sum Rule classification scheme. Results of comparative experiments indicate that the neutral-point technique loses relatively little classification information with respect to coincident training data, and is in fact preferable for independent kernels produced by different physical modalities due to its better error-cancellation properties.
this paper describes a method for extracting hyponyms from free text. In particular it explores two main matters. On the one hand, the possibility of reaching favorable results using only lexical extraction patterns. ...
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ISBN:
(纸本)9783540766308
this paper describes a method for extracting hyponyms from free text. In particular it explores two main matters. On the one hand, the possibility of reaching favorable results using only lexical extraction patterns. On the other hand, the usefulness of measuring the instance's confidences based on the pattern's confidences, and vice versa. Experimental results are encouraging because they show that the proposed method can be a practical high-precision approach for extracting hyponyms for a given set of concepts.
In this paper we describe the application of morphological shared-weight probabilistic neural networks to the problems of pattern classification in synthetic aperture radar (SAR) images. the feature extraction process...
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ISBN:
(纸本)9781424409723
In this paper we describe the application of morphological shared-weight probabilistic neural networks to the problems of pattern classification in synthetic aperture radar (SAR) images. the feature extraction process is learned by interaction withthe classification process. Feature extraction is performed using gray-scale hit- miss transforms that are independent of gray-level shifts. the classification process is performed by probabilistic neural networks(PNN). Classification experiments were carried out with SAR images of military objects. And classification results show MSPNN architecture to optimize object recognition versus processing time and veracity.
In this paper, a new human face recognition method based on anti-symmetrical biorthogonal wavelet transformation (ASBWT) and eigenface was proposed. First the anti-symmetrical biorthogonal wavelet is chosen to degrade...
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ISBN:
(纸本)9781424409723
In this paper, a new human face recognition method based on anti-symmetrical biorthogonal wavelet transformation (ASBWT) and eigenface was proposed. First the anti-symmetrical biorthogonal wavelet is chosen to degrade the face image dimension, meanwhile complete the process of face location and segmentation;And then human face is reverted through the face space of Eigenface, the traditional average human face is replaced in the within-class scatter matrix. this within-class scatter matrix is used to calculate within-class and between-class distance proportion as a rule function, calculate the twice eigenface through Discrete Karhunen-Loeve Transform (DKLT), and use Singular Value Decomposition (SVD) method to calculate the eigenvector. Finally we compute the weights and classify the face images. the results show that the proposed method has higher recognition rate and more robust than the traditional eigenface analysis method.
Feature selection is one of the most important issues in the fields such as data mining, patternrecognition and machine learning. In this study, a new feature selection approach that combines the Fisher criterion and...
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ISBN:
(纸本)9781424409723
Feature selection is one of the most important issues in the fields such as data mining, patternrecognition and machine learning. In this study, a new feature selection approach that combines the Fisher criterion and principal feature analysis (PFA) is proposed in order to identify the important (relevant and irredundant) feature subset. the Fisher criterion is used to remove features that are noisy or irrelevant, and then PFA is used to choose a subset of principal features. the proposed approach was evaluated in pattern classification on five publicly available datasets. the experimental results show that the proposed approach can largely reduce the feature dimensionality with little loss of classification accuracy.
In this paper, we propose a novel approach to retrieve line-patterns from large databases in a rotation and translation invariant manner, at the same time, tackle broken line problem. Line segments are extracted from ...
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this paper presents a two-step algorithm to perform automatic extraction of vessel tree on angiogram. Firstly, the approximate vessel centerline is modeled as marked point process with each point denoting a line segme...
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ISBN:
(纸本)9783540741954
this paper presents a two-step algorithm to perform automatic extraction of vessel tree on angiogram. Firstly, the approximate vessel centerline is modeled as marked point process with each point denoting a line segment. A Double Area prior model is proposed to incorporate the geometrical and topological constraints of segments through potentials on the interaction and the type of segments. Data likelihood allows for the vesselness of the points which the segment covers, which is computed through the Hessian matrix of the image convolved with 2-D Gaussian filter at multiple scales. Optimization is realized by simulated annealing scheme using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. Secondly, the extracted approximate vessel centerline, containing global geometry shape as well as location information of vessel, is used as important guide to explore the accurate vessel edges by combination with local gradient information of angiogram. this is implemented by morphological homotopy modification and watershed transform on the original gradient image. Experimental results of clinical digitized coronary angiogram are reported.
In this paper, the performance influencing class analysis (PICA) framework is proposed for performance analysis of patternrecognition systems dealing with data with great variety and diversity. through the PICA proce...
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this paper presents a new approach to constructing a neural tree with partial incremental learning capability. the proposed neural tree, called a (q) under bar uadratic-ne (u) under bar ron-b (a) under bar sed (n) und...
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ISBN:
(纸本)9781424409723
this paper presents a new approach to constructing a neural tree with partial incremental learning capability. the proposed neural tree, called a (q) under bar uadratic-ne (u) under bar ron-b (a) under bar sed (n) under bar eural (t) under bar ree (QUANT), is a tree structured neural network composed of neurons with quadratic neural-type junctions for pattern classification. the proposed QUANT integrates t e advantages of decision trees and neural networks. Via a batch-mode training algorithm, the QUANT grows a neural tree containing quadratic neurons in its nodes. these quadratic neurons recursively partition the feature space into hyper-ellipsoidal-shaped sub:-regions. the QUANT has the partial incremental capability so that it does not need to re-construct a new neural tree to accommodate new training data whenever new data are introduced to a trained QUANT. To demonstrate the performance of the proposed QUANT, several patternrecognition problems were tested.
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