When building an application that requires object class recognition, having enough data to learn from is critical for good performance, and can easily determine the success or fai...
This paper addresses the problem of classifying observations when features are context-sensitive, specifically when the testing set involves a context that is different from the t...
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
In fuzzy modeling (FM), the quantity and quality of the training set is crucial to properly grasp the behavior of the system being modeled. However, the available data are often n...
Pablo Carmona, Juan Luis Castro, Jose Manuel Zurit...
In this paper, a new approach to training set size reduction is presented. This scheme basically consists of defining a small number of prototypes that represent all the original ...
Abstract. In this paper, some new approaches to training set size reduction are presented. These schemes basically consist of defining a small number of prototypes that represent ...
Defeasible logic is a non-monotonic logic with applications in rule-based domains such as law. To ease the development and improve the accuracy of expert systems based on defeasib...
Learning Deterministic Finite Automata (DFA) is a hard task that has been much studied within machine learning and evolutionary computation research. This paper presents a new met...
In the typical nonparametric approach to classification in instance-based learning and data mining, random data (the training set of patterns) are collected and used to design a d...
Binay K. Bhattacharya, Kaustav Mukherjee, Godfried...
Abstract. The performance of financial forecasting with neural networks dependents on the particular training set. We design mean-change-point test to divide the original dataset i...