2004 Fiscal Year Final Research Report Summary
Adaptive Learning of Classifiers and Its Applications
| Project/Area Number |
15500088
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Shinshu University |
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Principal Investigator |
MARUYAMA Minoru Shinshu Univ., Dept.Inf.Eng, Assoc.Prof., 工学部, 助教授 (80283232)
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| Project Period (FY) |
2003 – 2004
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| Keywords | learning from examples / SVM / boosting / radial basis function / approximation / classification / virtual examples |
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| Research Abstract |
To recognize target objects in the image, technique of learning from examples are often exploited. To learn the good classifier, usually a lot of samples are required. On the other hand, human beings can recognize an object efficiently even if it is a novel object. To realize such ability on machines, it is necessary to develop a learning method to give classifiers which are efficient with respect to both recognition rate and recognition speed, from very small learning examples. Classifiers based on the non-linear functions are often used to recognize pattern in a image. Although their classification performance is quite good, their computational cost is often prohibitive. To overcome the difficulty, in this report, we first propose a method to recognize patterns in a image based on the hierarchical classifiers. It consists of two kinds of classifiers : simple and fast classifier, whose performance is not necessarily satisfactory, and the non-linear slow classifier, whose performance is quite good. With the use of first (bottom) classifier, many false targets are rejected. The second, complex classifier is applied to the remaining patterns. By this architecture, fast and reliable classifiers are made possible. As the non-linear classifiers, we use RBF-SVM. In the case of SVM, recognition speed depends on the number of support vectors. To improve the recognition speed further, we propose a method to approximate the SVM based on the sampling from the support vectors. We also propose methods to learn fast and reliable classifiers based on the boosting algorithm subject to the upper bound of the number of RBF centers. To learn the classifiers from the limited number of examples, we propose a method to utilize virtual examples. In the report, we first propose the synthesis method based on the simple geometric transformation. Then, to improve the quality of the samples for hand written pattems, we also propose amethod to generate pattems from online data.
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