Information Theory and Machine Learning

• Other Authors: Zheng, Lizhong (Editor), Tian, Chao (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Technology: general issues; History of engineering & technology; 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
• Online Access: DOAB: download the publication; DOAB: description of the publication

Summary

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.

Physical Description

1 electronic resource (254 p.)

ISBN

books978-3-0365-5308-5
9783036553078
9783036553085

Access

Open Access