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Titlebook: Algorithmic Learning Theory; 4th International Wo Klaus P. Jantke,Shigenobu Kobayashi,Takashi Yokomo Conference proceedings 1993 Springer-V

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發(fā)表于 2025-3-21 17:42:05 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Algorithmic Learning Theory
期刊簡稱4th International Wo
影響因子2023Klaus P. Jantke,Shigenobu Kobayashi,Takashi Yokomo
視頻videohttp://file.papertrans.cn/153/152985/152985.mp4
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Algorithmic Learning Theory; 4th International Wo Klaus P. Jantke,Shigenobu Kobayashi,Takashi Yokomo Conference proceedings 1993 Springer-V
影響因子This volume contains all the papers that were presented atthe Fourth Workshop on Algorithmic Learning Theory, held inTokyo in November 1993. In addition to 3 invited papers, 29papers were selected from 47 submitted extended abstracts.The workshop was the fourth in a series of ALTworkshops,whose focus is on theories of machine learning and theapplication of such theories to real-world learningproblems. The ALT workshops have been held annually since1990, sponsored by the Japanese Society for ArtificialIntelligence. The volume is organized into parts oninductive logic and inference, inductive inference,approximatelearning, query learning, explanation-basedlearning, and new learningparadigms.
Pindex Conference proceedings 1993
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沙發(fā)
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板凳
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https://doi.org/10.1007/978-3-8350-9143-6es in our procedure can be formally derived and its local convergence can be formally proved. Compared to existing methods, our procedure offers the following advantages: (1) better convergence characteristics than the standard Baum-Welch algorithm, (2) automatic reduction of model size to the right
地板
發(fā)表于 2025-3-22 06:50:02 | 只看該作者
On aggregating teams of learning machines,ich are required to be successful, but cannnot be TxtFex.-identified by any single machine. Additionally, aggregation ratios are also derived for finite identification of languages from positive data and for numerous criteria involving language learning from both positive and negative data.
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發(fā)表于 2025-3-22 16:58:10 | 只看該作者
A new algorithm for automatic configuration of Hidden Markov Models,es in our procedure can be formally derived and its local convergence can be formally proved. Compared to existing methods, our procedure offers the following advantages: (1) better convergence characteristics than the standard Baum-Welch algorithm, (2) automatic reduction of model size to the right
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發(fā)表于 2025-3-22 18:04:45 | 只看該作者
Klaus P. Jantke,Shigenobu Kobayashi,Takashi Yokomo
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