Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields including computer science. Good softwares have been demanded for various knowledge discovery tasks. For such softwares, we often need to develop efficient algorithms for handling huge data sets. Random sampling is one of ...

This paper presents Visalix, a Web-based interface aimed at facilitating human-computer cooperation in complex data analysis tasks. It implements an interactive visualization paradigm which assists users in matching their domain knowledge with the algorithmic power of data analysis and mining techniques. Visalix integrates a number of Visual Interactive Learning components for better understand...

Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields including computer science. Good softwares have been demanded for various knowledge discovery tasks. For such softwares, we often need to develop efficient algorithms for handling huge data sets. Random sampling is one of ...

The soft computing approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is aims to evolve efficiently some simple heuristics that can be created easily from the basic knowledge of the game. Integrating sphere knowledge into soft computation can enhance the performance of evolved algorithmic methodologies and quicken the...

We obtain an algorithmic construction of the isotropy lattice for a lifted action of a Lie group G on TM and T ∗M based only on the knowledge of G and its action on M . Some applications to symplectic geometry are also shown.

We present an algorithmic framework for learning multiple related tasks. Our framework exploits a form of prior knowledge that relates the output spaces of these tasks. We present PAC learning results that analyze the conditions under which such learning is possible. We present results on learning a shallow parser and named-entity recognition system that exploits our framework, showing consiste...

Formal concept analysis (FCA) can be used for designing concept lattices from binary data for knowledge discovery purposes. Pattern structures in FCA are able to deal with complex data. In addition, this formalism provides a concise and an efficient algorithmic view of the formalism of symbolic data analysis (SDA).