Titlebook: Artificial Neural Networks - ICANN 2010; 20th International C Konstantinos Diamantaras,Wlodek Duch,Lazaros S. Il Conference proceedings 201

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南极

过于平凡

The Support Feature Machine for Classifying with the Least Number of Featuresnorm of a separating hyperplane. Thus, a classifier with inherent feature selection capabilities is obtained within a single training run. Results on toy examples demonstrate that this method is able to identify relevant features very effectively.

Allure

Hidden Markov Model for Human Decision Process in a Partially Observable Environmentartially observable environment, humans can make appropriate decision by resolving the uncertainty. During decision making in an uncertain environment, resolving behaviors of the uncertainty and optimal behaviors to best suit for the environment are often incompatible, which is termed exploration-ex

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Representing, Learning and Extracting Temporal Knowledge from Neural Networks: A Case Study Intelligence. Temporal models are fundamental to describe the behaviour of computing and information systems. In addition, acquiring the description of the desired behaviour of a system is a complex task in several AI domains. In this paper, we evaluate a neural framework capable of adapting tempor

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Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradientsame of Go, which, despite its deceivingly simple rules, has eluded the development of artificial expert players. In this paper we attempt to tackle this challenge through a combination of two recent developments in Machine Learning. We employ Multi-Dimensional Recurrent Neural Networks with Long Sho

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Layered Motion Segmentation with a Competitive Recurrent Networkthat are governed by affine motion patterns. Using an energy-based competitive multilayer architecture based on non-negative activations and multiplicative update rules, we show how the network can perform segmentation tasks that require a combination of affine estimation with local integration and

VOC

refraction

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A Computational System of Metaphor Generation with Evaluation Mechanismrical expression of the form “target (A) like vehicle (B)”. A computational system consisting of a metaphor generation process and a metaphor evaluation process is developed. In the metaphor generation process, a metaphor generation model [1] outputs candidate nouns for vehicles from input expressio