Information Theory and Machine Learning

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
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