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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Bibliographic Details
Format:	eBook
Language:	English
Published:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:	History of engineering and technology Technology: general issues analytical error probability anomaly detection artificial general intelligence atypicality closed-loop transcription component overlap deep neural network distribution and federated learning empirical risk entropy fairness feature extraction finite state machines generalization error HGR maximal correlation hidden Markov models independence criterion independent and non-identically distributed features information criteria information theoretic learning information theory information-theoretic bounds interpretability K-means clustering Lempel-Ziv algorithm linear discriminative representation local information geometry long short-term memory lossless compression merging mixture components meta-learning minimax game minimum error entropy model compression model-based clustering overfitting pattern dictionary population risk rate distortion theory rate reduction recurrent neural networks reservoir computers separation criterion spiking neural network supervised classification time series prediction vector quantization
Online Access:	Open Access: DOAB: description of the publication Open Access: DOAB, download the publication

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Open Access: DOAB: description of the publication
Open Access: DOAB, download the publication

Information Theory and Machine Learning

Internet

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