H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 110 Citations 121,042 290 World Ranking 87 National Ranking 56

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Alexander J. Smola focuses on Artificial intelligence, Kernel method, Support vector machine, Machine learning and Kernel. His Artificial intelligence research is multidisciplinary, relying on both Margin, Data mining and Pattern recognition. His study looks at the intersection of Kernel method and topics like Reproducing kernel Hilbert space with Applied mathematics and String kernel.

His work on Least squares support vector machine is typically connected to Novelty detection as part of general Support vector machine study, connecting several disciplines of science. In his study, Synchronization and Autoregressive model is inextricably linked to State, which falls within the broad field of Machine learning. His Kernel research incorporates elements of Algorithm, Probability distribution and Kernel.

His most cited work include:

  • A tutorial on support vector regression (7388 citations)
  • Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (7343 citations)
  • Nonlinear component analysis as a kernel eigenvalue problem (6463 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Artificial intelligence, Machine learning, Algorithm, Support vector machine and Pattern recognition. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Theoretical computer science and Data mining. His research combines Multi-task learning and Machine learning.

Alexander J. Smola frequently studies issues relating to Function and Algorithm. His study in Support vector machine is interdisciplinary in nature, drawing from both Regularization, Mathematical optimization and Feature vector. His Kernel method study frequently draws connections to other fields, such as Applied mathematics.

He most often published in these fields:

  • Artificial intelligence (45.08%)
  • Machine learning (27.46%)
  • Algorithm (16.48%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (45.08%)
  • Machine learning (27.46%)
  • Theoretical computer science (12.36%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Machine learning, Theoretical computer science, Reinforcement learning and Deep learning. His research integrates issues of Natural language processing and Pattern recognition in his study of Artificial intelligence. His studies deal with areas such as State and Component as well as Machine learning.

The various areas that Alexander J. Smola examines in his Theoretical computer science study include Embedding, Simple and Graph. His Reinforcement learning research includes elements of Control, Sample, Graph and Benchmark. While the research belongs to areas of Automatic summarization, he spends his time largely on the problem of Leverage, intersecting his research to questions surrounding Algorithm.

Between 2016 and 2021, his most popular works were:

  • Sampling Matters in Deep Embedding Learning (427 citations)
  • Deep Sets (401 citations)
  • Recurrent Recommender Networks (363 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Alexander J. Smola mostly deals with Artificial intelligence, Machine learning, Theoretical computer science, Deep learning and Graph. His Artificial intelligence research integrates issues from Natural language processing and Pattern recognition. Alexander J. Smola interconnects Python and Raw data in the investigation of issues within Machine learning.

His Theoretical computer science research includes themes of Exponential number, Permutation, Point cloud and Knowledge base. As a part of the same scientific study, Alexander J. Smola usually deals with the Deep learning, concentrating on Noise and frequently concerns with Knowledge graph, Information retrieval and Human voice. His Graph research is multidisciplinary, incorporating elements of Graph, Random walk and Reinforcement learning.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

Bernhard Scholkopf;Alexander J. Smola.
Journal of the American Statistical Association (2001)

10934 Citations

A tutorial on support vector regression

Alex J. Smola;Bernhard Schölkopf.
Statistics and Computing (2004)

10475 Citations

Nonlinear component analysis as a kernel eigenvalue problem

Bernhard Schölkopf;Alexander Smola;Klaus-Robert Müller.
Neural Computation (1998)

9108 Citations

Advances in kernel methods: support vector learning

Bernhard Schölkopf;Christopher J. C. Burges;Alexander J. Smola.
international conference on neural information processing (1999)

6266 Citations

Estimating the Support of a High-Dimensional Distribution

Bernhard Schölkopf;John C. Platt;John C. Shawe-Taylor;Alex J. Smola.
Neural Computation (2001)

4957 Citations

Support Vector Regression Machines

Harris Drucker;Christopher J. C. Burges;Linda Kaufman;Alex J. Smola.
neural information processing systems (1996)

3663 Citations

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing

Vladimir Vapnik;Steven E. Golowich;Alex J. Smola.
neural information processing systems (1996)

3367 Citations

New Support Vector Algorithms

Bernhard Schölkopf;Alex J. Smola;Robert C. Williamson;Peter L. Bartlett.
Neural Computation (2000)

3250 Citations

Kernel Principal Component Analysis

Bernhard Schölkopf;Alexander J. Smola;Klaus-Robert Müller.
international conference on artificial neural networks (1997)

2381 Citations

Hierarchical Attention Networks for Document Classification

Zichao Yang;Diyi Yang;Chris Dyer;Xiaodong He.
north american chapter of the association for computational linguistics (2016)

2358 Citations

If you think any of the details on this page are incorrect, let us know.

Contact us

Best Scientists Citing Alexander J. Smola

Bernhard Schölkopf

Bernhard Schölkopf

Max Planck Institute for Intelligent Systems

Publications: 220

Klaus-Robert Müller

Klaus-Robert Müller

Technical University of Berlin

Publications: 176

Masashi Sugiyama

Masashi Sugiyama

University of Tokyo

Publications: 137

Johan A. K. Suykens

Johan A. K. Suykens

KU Leuven

Publications: 134

arthur gretton

arthur gretton

University College London

Publications: 129

Eric P. Xing

Eric P. Xing

Carnegie Mellon University

Publications: 116

Jose C. Principe

Jose C. Principe

University of Florida

Publications: 104

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 102

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 96

Ivor W. Tsang

Ivor W. Tsang

University of Technology Sydney

Publications: 94

Qiang Yang

Qiang Yang

Hong Kong University of Science and Technology

Publications: 82

Rong Jin

Rong Jin

Alibaba Group (China)

Publications: 82

Francis Bach

Francis Bach

French Institute for Research in Computer Science and Automation - INRIA

Publications: 81

Michael I. Jordan

Michael I. Jordan

University of California, Berkeley

Publications: 81

Gustau Camps-Valls

Gustau Camps-Valls

University of Valencia

Publications: 80

Zhi-Hua Zhou

Zhi-Hua Zhou

Nanjing University

Publications: 80

Something went wrong. Please try again later.