A. Akkus. Batch Learning of Disjoint Feature Intervals. Bilkent University, Dept. of Computer Engineering and Information Science, MSc. Thesis, 1996. 147  Home/Search   Document Details and Download   Summary   Related Articles

.... a probability distribution over all classes instead of a categorical classification [45] The representation of concepts learned by VFI classifiers is similar to that of other concept learning models using feature projection based knowledge representation scheme such as CFP [32] FIL algorithms [7], COFI [73] and k NNFP [8] all of which are described in detail in Chapter 3. The voting scheme used to classify a new instance has also evolved from the voting schemes used in these related methods. Chapter 4 explains the details of this new classification method. Since induction of ....

....example is defined as a vector of feature values along with a label which represents the category (class) of the example. Knowledge representation of exemplar based models can be maintained as representative instances [2, 5] hyperrectangles [62, 63] or feature projection based representations [7, 8, 22, 32, 73]. Unlike Explanation Based Generalization (EBG) 19, 50] little or no domain specific knowledge is required in exemplar based learning. Figure 2.1 presents a hierarchical classification of exemplar based learning models. Instance Based Learning (IBL) and Exemplar Based Generalization ....

[Article contains additional citation context not shown here]

A. Akkus. Batch Learning of Disjoint Feature Intervals. Bilkent University, Dept. of Computer Engineering and Information Science, MSc. Thesis, 1996. 147

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC