Nearest neighbor rule or k-nearest neighbor rule is a technique of nonparametric pattern recognition. Its algorithm is simple and error is smaller than twice the Bayes error if there are enough training samples. However, it requires enormous computational quantities that is proportional to the number of samples and the number of dimensions of feature vector. In this paper, a fast algorithm for ...

For the task of classification, the quality of rule set is usually evaluated as a whole rather than evaluating the quality of a single rule. The present investigation proposes a hybrid classifier named FDF. By redefining information gain from the general sense of Information theory, rule sets are built and combined to be decision forest by down-top learning strategy. The finial decision tree no...

This report reviews various optimum decision rules for pattern recognition, namely, Bayes rule, Chow's rule (optimum error-reject tradeo ), and a recently proposed class-selective rejection rule. The latter provides an optimum tradeo between the error rate and the average number of (selected) classes. A new general relation between the error rate and the average number of classes is presented. ...

In this paper we consider some bounds for lower previsions that are either coherent or centered convex. As for coherent conditional previsions, we adopt a structure-free version of Williams’ coherence, which we compare with Williams’ original version and with other coherence concepts. We then focus on bounds concerning the classical product and Bayes’ rules. After discussing some implications o...

The complex word identification task refers to the process of identifying difficult words in a sentence from the perspective of readers belonging to a specific target audience. This task has immense importance in the field of lexical simplification. Lexical simplification helps in improving the readability of texts consisting of challenging words. As a participant of the SemEval-2016: Task 11 s...

Several alternative decision rules have been proposed for how individuals assess and choose options, such as mates and territories. Three of these rules are the threshold rule, where individuals choose the first option that exceeds a preset level of quality, the best-of-n rule, where individuals assess a fixed number of options and then choose the best of those options, and the comparative Baye...

We apply the method of complexity regularization to learn concepts from large concept classes. The method is shown to automatically find a good balance between the approximation error and the estimation error. In particular, the error probability of the obtained classifier is shown to decrease as O(dm) to the achievable optimum, for large nonparametric classes of distributions, as the sample si...

In the rough set theory with every decision rule two conditional probabilities, called certainty and co erage factors, are associated. These two factors are closely related with the lower and the upper approximation of a set, basic notions of rough set theory. It is shown that these two factors satisfy the Bayes’ theorem. The Bayes’ theorem in our case simply shows some relationship in the data...