By Yixin Chen
Within the early Nineteen Nineties, the institution of the net introduced forth a innovative perspective of knowledge garage, distribution, and processing: the realm large net is changing into a big and increasing dispensed electronic library. in addition to the improvement of the net, snapshot indexing and retrieval have grown into examine components sharing a imaginative and prescient of clever brokers. a long way past net looking out, photo indexing and retrieval can possibly be utilized to many different parts, together with biomedicine, house technology, biometric identity, electronic libraries, the army, schooling, trade, tradition and leisure. Machine studying and Statistical Modeling methods to picture Retrieval describes numerous techniques of integrating desktop studying and statistical modeling into a picture retrieval and indexing procedure that demonstrates promising effects. the themes of this publication replicate authors' reviews of computer studying and statistical modeling dependent snapshot indexing and retrieval. This e-book comprises designated references for additional interpreting and study during this box to boot.
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Additional info for Machine Learning and Statistical Modeling Approaches to Image Retrieval
1 with nonzero threshold there exists a binary fuzzy classifier that has the same decision rule but zero threshold. The membership functions for a binary fuzzy classifier defined above could be any function from to [0, 1]. However, too much flexibility on the model could make effective learning (or training) unfeasible. Therefore we narrow our interests to a class of membership functions, which are generated from translation of reference functions [Dubois and Prade, 1978], and the classifiers defined on them.
Z. Wang, “Support Vector Learning for Fuzzy Rule-Based Classification Systems,” IEEE Transactions on Fuzzy Systems, 11(6), 2003. ©2004 IEEE. , it is essentially a mapping from the input space to the set of class labels. In this section, we are interested in binary fuzzy classifiers defined as follows. 18). 21), is a threshold. 1 with nonzero threshold there exists a binary fuzzy classifier that has the same decision rule but zero threshold. The membership functions for a binary fuzzy classifier defined above could be any function from to [0, 1].
Nevertheless, in many applications a crisp output value is required. For example, the output of a fuzzy classifier should be the class label corresponding to a given input, while the prediction made by a fuzzy function approximator is usually a real number. The mapping from B to some real number is realized by a defuzzifier. Several commonly used defuzzification strategies may be described as the max criterion (MC), the mean of maximum (MOM), and the center of area (COA) [Lee, 1990]. For a given input the MC finds the global maximizer of the MOM computes the mean value of all local maximizers of and COA defines the output as 2 An dimensional fuzzy set is a fuzzy set in with membership function 36 LEARNING AND MODELING - IMAGE RETRIEVAL Consider an additive fuzzy system with fuzzy rules of the form where is a fuzzy set with membership function If we choose product as the fuzzy conjunction operator and COA defuzzification, then the model becomes a special form of the Takagi-Sugeno (TS) fuzzy model [Takagi and Sugeno, 1985], and the input output mapping, of the model is defined as where is the input.