Information Theory and Machine Learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (254 p.) |
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(CKB)5670000000391640 (oapen)https://directory.doabooks.org/handle/20.500.12854/93254 (EXLCZ)995670000000391640 |
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Zheng, Lizhong edt Information Theory and Machine Learning MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (254 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems. English Technology: general issues bicssc History of engineering & technology bicssc supervised classification independent and non-identically distributed features analytical error probability empirical risk generalization error K-means clustering model compression population risk rate distortion theory vector quantization overfitting information criteria entropy model-based clustering merging mixture components component overlap interpretability time series prediction finite state machines hidden Markov models recurrent neural networks reservoir computers long short-term memory deep neural network information theory local information geometry feature extraction spiking neural network meta-learning information theoretic learning minimum error entropy artificial general intelligence closed-loop transcription linear discriminative representation rate reduction minimax game fairness HGR maximal correlation independence criterion separation criterion pattern dictionary atypicality Lempel–Ziv algorithm lossless compression anomaly detection information-theoretic bounds distribution and federated learning 3-0365-5307-X Tian, Chao edt Zheng, Lizhong oth Tian, Chao oth |
| language |
English |
| format |
eBook |
| author2 |
Tian, Chao Zheng, Lizhong Tian, Chao |
| author_facet |
Tian, Chao Zheng, Lizhong Tian, Chao |
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l z lz c t ct |
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HerausgeberIn Sonstige Sonstige |
| title |
Information Theory and Machine Learning |
| spellingShingle |
Information Theory and Machine Learning |
| title_full |
Information Theory and Machine Learning |
| title_fullStr |
Information Theory and Machine Learning |
| title_full_unstemmed |
Information Theory and Machine Learning |
| title_auth |
Information Theory and Machine Learning |
| title_new |
Information Theory and Machine Learning |
| title_sort |
information theory and machine learning |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2022 |
| physical |
1 electronic resource (254 p.) |
| isbn |
3-0365-5308-8 3-0365-5307-X |
| illustrated |
Not Illustrated |
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AT zhenglizhong informationtheoryandmachinelearning AT tianchao informationtheoryandmachinelearning |
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