D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 69 Citations 59,816 154 World Ranking 1200 National Ranking 695

Research.com Recognitions

Awards & Achievements

2010 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the theory and practice of efficient machine learning algorithms.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Machine learning, Algorithm, Boosting and Mathematical optimization. The study incorporates disciplines such as Natural language processing, Query expansion and Pattern recognition in addition to Artificial intelligence. His Machine learning research focuses on Synthetic data and how it relates to Quadratic programming, Learnability, Coding and Special case.

His study focuses on the intersection of Algorithm and fields such as Margin Infused Relaxed Algorithm with connections in the field of Decision problem and Lemma. His study on Optimization problem, Interior point method and Gradient projection is often connected to Online learning as part of broader study in Mathematical optimization. His studies in Optimization problem integrate themes in fields like Regularization, Kernel and Support vector machine.

His most cited work include:

  • Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (5921 citations)
  • Improved boosting algorithms using confidence-rated predictions (2475 citations)
  • Feature-rich part-of-speech tagging with a cyclic dependency network (2474 citations)

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

His primary areas of investigation include Artificial intelligence, Algorithm, Machine learning, Pattern recognition and Mathematical optimization. His Artificial intelligence study incorporates themes from Online algorithm and Natural language processing. His study in Algorithm is interdisciplinary in nature, drawing from both Mixture model, Supervised learning, Probabilistic logic and Theoretical computer science.

His work on Ranking SVM, Ranking, Text categorization and Kernel method as part of general Machine learning study is frequently linked to Simple, bridging the gap between disciplines. His Pattern recognition study integrates concerns from other disciplines, such as Margin and Iterative method. The Mathematical optimization study combines topics in areas such as Online machine learning, Regularization and Applied mathematics.

He most often published in these fields:

  • Artificial intelligence (52.87%)
  • Algorithm (29.30%)
  • Machine learning (29.30%)

What were the highlights of his more recent work (between 2011-2021)?

  • Artificial intelligence (52.87%)
  • Pattern recognition (22.93%)
  • Algorithm (29.30%)

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

His primary areas of study are Artificial intelligence, Pattern recognition, Algorithm, Machine learning and Representation. Yoram Singer studies Artificial intelligence, namely Regularization. He interconnects Embedding, Zero shot learning, Word embedding and Convex combination in the investigation of issues within Pattern recognition.

His Algorithm research incorporates elements of Sparse matrix and Robustness. His Empirical risk minimization and Ranking study in the realm of Machine learning connects with subjects such as Subject matter and Implementation. Many of his research projects under Mathematical optimization are closely connected to Newton's method in optimization with Newton's method in optimization, tying the diverse disciplines of science together.

Between 2011 and 2021, his most popular works were:

  • Zero-Shot Learning by Convex Combination of Semantic Embeddings (579 citations)
  • Local Low-Rank Matrix Approximation (133 citations)
  • Local collaborative ranking (112 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

Artificial intelligence, Pattern recognition, Mathematical optimization, Algorithm and Word embedding are his primary areas of study. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Matrix norm and Collaborative filtering. His Machine learning research is multidisciplinary, incorporating elements of Cognitive neuroscience of visual object recognition, Inference, Categorization and Statistics.

In his study, which falls under the umbrella issue of Mathematical optimization, Representation and Kernel smoother is strongly linked to Eigendecomposition of a matrix. His work carried out in the field of Algorithm brings together such families of science as Learnability and Robustness. His Word embedding study combines topics in areas such as Convex combination, Image transformation and Zero shot 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

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

John Duchi;Elad Hazan;Yoram Singer.
Journal of Machine Learning Research (2011)

9879 Citations

Improved boosting algorithms using confidence-rated predictions

Robert E. Schapire;Yoram Singer.
conference on learning theory (1998)

4506 Citations

Feature-rich part-of-speech tagging with a cyclic dependency network

Kristina Toutanova;Dan Klein;Christopher D. Manning;Yoram Singer.
north american chapter of the association for computational linguistics (2003)

4123 Citations

BoosTexter: A Boosting-based Systemfor Text Categorization

Robert E. Schapire;Yoram Singer.
Machine Learning (2000)

3084 Citations

An efficient boosting algorithm for combining preferences

Yoav Freund;Raj Iyer;Robert E. Schapire;Yoram Singer.
Journal of Machine Learning Research (2003)

2922 Citations

On the algorithmic implementation of multiclass kernel-based vector machines

Koby Crammer;Yoram Singer.
Journal of Machine Learning Research (2002)

2755 Citations

Reducing multiclass to binary: a unifying approach for margin classifiers

Erin L. Allwein;Robert E. Schapire;Yoram Singer.
Journal of Machine Learning Research (2001)

2638 Citations

Pegasos: primal estimated sub-gradient solver for SVM

Shai Shalev-Shwartz;Yoram Singer;Nathan Srebro;Andrew Cotter.
Mathematical Programming (2011)

2617 Citations

Online Passive-Aggressive Algorithms

Koby Crammer;Koby Crammer;Ofer Dekel;Joseph Keshet;Shai Shalev-Shwartz.
Journal of Machine Learning Research (2006)

2213 Citations

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM

Shai Shalev-Shwartz;Yoram Singer;Nathan Srebro.
international conference on machine learning (2007)

1502 Citations

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