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Titlebook: Algorithmic Learning for Knowledge-Based Systems; GOSLER Final Report Klaus P. Jantke,Steffen Lange Book 1995 Springer-Verlag Berlin Heidel

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期刊全称Algorithmic Learning for Knowledge-Based Systems
期刊简称GOSLER Final Report
影响因子2023Klaus P. Jantke,Steffen Lange
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Algorithmic Learning for Knowledge-Based Systems; GOSLER Final Report Klaus P. Jantke,Steffen Lange Book 1995 Springer-Verlag Berlin Heidel
影响因子This book is the final report on a comprehensive basic research project, named GOSLER on algorithmic learning for knowledge-based systems supported by the German Federal Ministry of Research and Technology during the years 1991 - 1994. This research effort was focused on the study of fundamental learnability problems integrating theoretical research with the development of tools and experimental investigation..The contributions by 11 participants in the GOSLER project is complemented by contributions from 23 researchers from abroad. Thus the volume provides a competent introduction to algorithmic learning theory.
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Classification using information,e learner can access. To increase the models ability to access information, we will give it the ability to ask more powerful questions. To decrease the models ability to access information, we will bound the number of mindchanges it may make.
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Probabilistic versus deterministic memory limited learning,
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https://doi.org/10.1007/978-3-662-39676-6e learner can access. To increase the models ability to access information, we will give it the ability to ask more powerful questions. To decrease the models ability to access information, we will bound the number of mindchanges it may make.
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Der Buchmarkt als Kommunikationsraum the efficiency of learning algorithms..The paper mainly emphasis to provide a comprehensive summary of results recently obtained, and of . developed. Finally, throughout our guided tour we discuss the question of what a natural language learning algorithm might look like.
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Planung von Braunkohlentagebauen,ing consistently in polynomial time. In particular, we present a natural learning problem and prove that it can be solved in polynomial time if and only if the algorithm is allowed to work inconsistently.
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https://doi.org/10.1007/978-3-476-03346-8ase-based variant. The achieved results strengthen the conjecture of the equivalence of the learning power of symbolic and case-based methods and show the interdependency between the measure used by a case-based algorithm and the target concept.
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