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...

Повний опис

Збережено в:

Бібліографічні деталі
Формат:	Online
Мова:	Англійська
Опубліковано:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Предмети:	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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology
Онлайн доступ:	ONIX_20221025_9783036553078_107
Теги:	Додати тег Немає тегів, Будьте першим, хто поставить тег для цього запису!

Будьте першим, хто залишить коментар!

Information Theory and Machine Learning

Схожі ресурси