HCV is a heuristic attribute-based induction algorithm based an the newly-developed extension matrix approach. By dividing the positive examples (PE) of a specific class in a given example set into intersecting groups...
详细信息
HCV is a heuristic attribute-based induction algorithm based an the newly-developed extension matrix approach. By dividing the positive examples (PE) of a specific class in a given example set into intersecting groups and adopting a set of strategies to find a heuristic conjunction formula in each group which covers all the group's positive examples and none of the negative examples (NE), it can find a covering formula in the form of variable-valued logic for PE against NE in low-order polynomial time. The original algorithm performs quite well with those data sets where noise and continuous data are not of major concern. However, its performance decreases when the data sets are noisy and contain continuous attributes. This paper presents noise handling techniques developed and implemented in HCV (Version 2.0), a noise tolerant version of the HCV algorithm, and provides a performance comparison of HCV with other inductive algorithms C4.5 and NewID in noisy and continuous domains.
We prove that maximum H-matching (the problem of determining the maximum number of node-disjoint copies of the fixed graph H contained in a variable graph) is a Max SNP-hard problem for any graph H that has three or m...
详细信息
We prove that maximum H-matching (the problem of determining the maximum number of node-disjoint copies of the fixed graph H contained in a variable graph) is a Max SNP-hard problem for any graph H that has three or more nodes in some connected component. If H is connected and the degrees of the nodes in H are bounded by a constant the problem is Max SNP-complete.
作者：
LANSNER, ALILJENSTROM, HSANS
Studies of Artificial Neural Systems Department of Numerical Analysis and Computing Science Royal Institute of Technology S-100 44 Stockholm Sweden
The recent developments in computer capacity and algorithms, together with a tremendous growth of data in neuroscience have dramatically improved the possibilities of modeling and simulating certain brain structures a...
The recent developments in computer capacity and algorithms, together with a tremendous growth of data in neuroscience have dramatically improved the possibilities of modeling and simulating certain brain structures and activities with a considerable degree of realism. Although there is still a long way to go, some claim that we will one day be able to create artificial ''brains'' with similar capacity to the human brain, perhaps even surpassing it. Here we focus on these perspectives, discussing the potentials and limitations of today's computer models, and how far they might be able to take us.
Computer simulation of neuronal networks is rapidly becoming accepted as a powerful tool in neuroscience. We illustrate the trends in this field by looking at motor generation and control, with examples from recent mo...
详细信息
An intertwine of two graphs H and H′ is a graph G such that G contains both H and H′ as minors, but no proper minor of G contains both H and H′ as minors. We give an upper bound on the size of an intertwine of two ...
详细信息
We consider the combinatorial problem MAXFLS which consists, given a system of linear relations, of finding a maximum feasible subsystem, that is a solution satisfying as many relations as possible. The approximabilit...
详细信息
暂无评论