In this paper, we study patternrecognition using stochastic artificial neural networks (SANN). A learning system can be defined by three rules: the encoding rule, the rule of internal change, and the quantization rul...
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ISBN:
(纸本)0819413208
In this paper, we study patternrecognition using stochastic artificial neural networks (SANN). A learning system can be defined by three rules: the encoding rule, the rule of internal change, and the quantization rule. In our system, the data encoding is to store an image in a stable distribution of a SANN. Given an input image f (epsilon) F, one can find a SANN t (epsilon) T such that the equilibrium distribution of this SANN is the given image f. Therefore, the input image, f, is encoded into a specification of a SANN, t. This mapping from F (image space) to T (parameter space of SANN) defines SANN transformation. SANN transformation encodes an input image into a relatively small vector which catches the characteristics of the input vector. The internal space T is the parameter space of SANN. The internal change rule of our system uses a local minima algorithm to encode the input data. The output data of the encoding stage is a specification of a stochastic dynamical system. The quantization rule divides the internal data space T by sample data.
This paper deals with a two-step segmentation algorithm for 2-D convex objects. First the objects are approximated by an elliptic shape description, and then the boundary of the object is refined using dynamic program...
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ISBN:
(纸本)0819413208
This paper deals with a two-step segmentation algorithm for 2-D convex objects. First the objects are approximated by an elliptic shape description, and then the boundary of the object is refined using dynamic programming. The reason for refinement is accurate shape classification.
In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of param...
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ISBN:
(纸本)0819413208
In this work, feature extraction algorithms are proposed to extract the tissue characterization parameters from liver images. Then the resulting parameter set is further processed to obtain the minimum number of parameters representing the most discriminating pattern space for classification. This preprocessing step was applied to over 120 pathology-investigated cases to obtain the learning data for designing the classifier. The extracted features are divided into independent training and test sets and are used to construct both statistical and neural classifiers. The optimal criteria for these classifiers are set to have minimum error, ease of implementation and learning, and the flexibility for future modifications. Various algorithms for implementing various classification techniques are presented and tested on the data. The best performance was obtained using a single layer tensor model functional link network. Also, the voting k-nearest neighbor classifier provided comparably good diagnostic rates.
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