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Incorporation of a specific number of stable fixed points (attractors) in a neural network is an important issue in many applications, including image processing and pattern recognition. The vast majority of model requires hundred presentation of the patterns before the learning is converged. This increases the simulation time considerably and thus limit their practical applications. In this paper,...
Pattern recognition systems usually have a relatively small number of patterns to be recognized. As a rule the number of handwritten symbols, number of phonemes or number of human faces are of the order of some dozens. But sometimes the pattern recognition task demands much more classes. For example, a continuous speech recognition system can be created on the base of syllables; a handwriting recognition...
A hip protector system using an airbag for prevention of femoral neck fractures is under developing by our group. In the system, instance detection of falling motions by using an appropriate on-line algorithm based on sensor signals is required. The purpose of the present paper is to propose online distinction procedures for human falling motions based on the machine learning, such as the support...
The objective of study is to develop intelligent decision support system to aid radiologist in diagnosis using pattern recognition techniques to estimate diagnostic function. In this study 3 approaches investigated namely statistical, neural networks and optimization techniques which were applied on the Wisconsin dataset. Trained neural networks, with the data set used as input, improve on the independent...
As a powerful machine learning approach for pattern recognition problems, support vector machine is known to have good generalization ability. Based on the index system of enterprise's self-fulfillment capability, a new integrated evaluation model is established by using support vector regression method. The method has advantages of accuracy, convenience, reliability and rapidity. The method is illustrated...
The self-organizing map that Kohonen has proposed maps high-dimensional vector data to low-dimensional space by phase conservation. And, it generates the feature map that visually catches the similarity among data. In addition, the reference vector where the unit in a competitive layer of SOM is achieved can interpolate an intermediate vector of the input vector data. In the pattern recognition of...
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