Foundations and Trends in Machine Learning

ISSN: 1935-8237

Journal Home

Foundations and Trends in Machine Learning Q1 Unclaimed

Now Publishers Inc United States

Unfortunately this journal has not been claimed yet. For this reason, some information may be unavailable.

Electronic publishing has given researchers instant access to more articles than ever before. But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic? To address this problem Foundations and Trends® in Machine Learning publishes high-quality survey and tutorial monographs of the field. Foundations and Trends® in Machine Learning publishes survey and tutorial articles on the theory, algorithms and applications of machine learning It has an SJR impact factor of 13,775.

Type: Journal

Type of Copyright:

Languages:

Open Access Policy: Non Open Access

Type of publications:

Publication frecuency: -

Categories: Artificial Intelligence (Q1) Human-Computer Interaction (Q1) Software (Q1)

Price

- €

Inmediate OA

- €

Embargoed OA

0 €

Non OA

Metrics

13,775

SJR Impact factor

36

H Index

2

Total Docs (Last Year)

15

Total Docs (3 years)

173

Total Refs

851

Total Cites (3 years)

15

Citable Docs (3 years)

39.1

Cites/Doc (2 years)

86.5

Ref/Doc

Comments

No comments ... Be the first to comment!

Publication speed *

Review quality *

Edition and editorial team *

Institutional email or ORCID *

* The email or ORCID field is only to validate the authenticity of the comment, the identity will not be public. .

Other journals with similar parameters

IEEE Transactions on Evolutionary Computation Q1
IEEE Transactions on Cybernetics Q1
Synthesis Lectures on Communication Networks Q1
Information Fusion Q1
IEEE Transactions on Pattern Analysis and Machine Intelligence Q1

Compare this journals

Aims and Scope

Best articles by citations

An Introduction to Matrix Concentration Inequalities

Kernel Mean Embedding of Distributions: A Review and Beyond

Explicit-Duration Markov Switching Models

Learning Deep Architectures for AI

A Survey of Statistical Network Models

Property Testing: A Learning Theory Perspective

Kernels for Vector-Valued Functions: A Review

A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning

A Tutorial on Thompson Sampling

Explaining the Success of Nearest

Optimization with Sparsity-Inducing Penalties

Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

SHOW MORE ARTICLES

Computational Optimal Transport

Introduction to Multi-Armed Bandits

Patterns of Scalable Bayesian Inference

From Bandits to Monte-Carlo Tree Search: The Optimistic Principle Applied to Optimization and Planning

An Introduction to Variational Autoencoders

An Introduction to Wishart Matrix Moments

Backward Simulation Methods for Monte Carlo Statistical Inference

Graphical Models, Exponential Families, and Variational Inference

Randomized Algorithms for Matrices and Data

Learning Representation and Control in Markov Decision Processes: New Frontiers

Online Learning and Online Convex Optimization

Determinantal Point Processes for Machine Learning