Electronic nose as a mimic biological olfactory system of intelligent devices can be reliably and quickly realize the simple or complex odor discrimination. Compared with the traditional gas chromatography and other expensive gas analysis equipment, it is simple, reliable results, and for field testing, and therefore widely used in food, agriculture, health care, environmental monitoring
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Support vector machine (SVM) has solid theoretical basis of Statistical learning theory (SLT), perfect form of mathematics, intuitive geometric explanation and good generalization ability. SVM has been a powerful tool for solving many problems in the domain of data mining. However, for most of practical applications, there are many kinds of complex structural data, such
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Local Learning algorithm has a small generalization error and is conerned inMachine Learning filed in recent years. Compared with the global classifier, It paysattition to the local distribution of samples and the better accuracy can be obtained by aproper choice of the locality parameter in the local classifier, which makes locallearning algorithms very appealing for
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With the increasing improvement on functions of missiles, satellites and other large systems, requirements of reliability for the systems are also increasing. Electronic equipments are the core control units of large systems, so requirements of reliability for them are strict. Analog circuits take a small proportion of electronic circuits, but they are easier to go
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This thesis is a simple trial on depicting the reliance of machine learning algorithms on the distribution features of data set. It takes three research results as examples. The first example is about the decision tree algorithm. It is discovered that under the circumstances of the “same side splitting”, some purity functions used will become
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Support Vector Machine comes down to solving a quadratic programming problem,. Newton’s method, interior point method and other classical optimization algorithms are good solutions. But when dealing with large-scale data, there are some shortcomings such as a memory limitation, too long training time and other issues. After years of research, it appears many excellent algorithms
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Support Vector Machine (SVM) developed to be the core of statistical learning theory in 1990s, it is a new machine learning method proposed by V.Vapnik of AT&T Bell Laboratories, which solves machine learning problems by means of optimization methods, integrates optimal hyperplane, mercer kernel function, convex quadratic programming, sparse solutions and relaxation etc. several techniques,
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With the increasing improvement on functions of missiles, satellites and other large systems, requirements of reliability for the systems are also increasing. Electronic equipments are the core control units of large systems, so requirements of reliability for them are strict. Analog circuits take a small proportion of electronic circuits, but they are easier to go
(Read More…)
The rapid development and application of Internet brought us great convenience,but also opened the door for hackers, brought us a huge security risk. Facing thegrowing number of attacks, how to identify a variety of attacks, quickly, accurately andeffectively, is a pressing problem. We need urgently to develop a new intrusiondetection method to deal with the
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Support Vector Machine comes down to solving a quadratic programming problem,. Newton’s method, interior point method and other classical optimization algorithms are good solutions. But when dealing with large-scale data, there are some shortcomings such as a memory limitation, too long training time and other issues. After years of research, it appears many excellent algorithms
(Read More…)
