D-Index & Metrics Best Publications

D-Index & Metrics

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 62 Citations 36,921 153 World Ranking 1347 National Ranking 769

Research.com Recognitions

Awards & Achievements

2003 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the development and understanding of machine learning algorithms and their relationship to human learning.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Michael J. Pazzani mainly investigates Artificial intelligence, Machine learning, World Wide Web, Data mining and Recommender system. Michael J. Pazzani works in the field of Artificial intelligence, focusing on Naive Bayes classifier in particular. His Machine learning research includes elements of Multi-task learning, Inductive bias and Domain theory.

His work on Web page and Web access as part of general World Wide Web research is frequently linked to Newspaper, bridging the gap between disciplines. Michael J. Pazzani has included themes like Representation and Cluster analysis in his Data mining study. Michael J. Pazzani interconnects Property and Knowledge extraction in the investigation of issues within Recommender system.

His most cited work include:

  • On the Optimality of the Simple Bayesian Classifier under Zero-One Loss (2520 citations)
  • Content-based recommendation systems (1623 citations)
  • Dimensionality reduction for fast similarity search in large time series databases (1196 citations)

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

His primary areas of investigation include Artificial intelligence, Machine learning, World Wide Web, Naive Bayes classifier and Data mining. His research ties Natural language processing and Artificial intelligence together. His research integrates issues of Algorithm, Set and Bayesian probability in his study of Machine learning.

As part of his studies on World Wide Web, Michael J. Pazzani frequently links adjacent subjects like Information retrieval. His biological study spans a wide range of topics, including Classifier and Bayesian statistics. His Data mining study combines topics in areas such as Search engine indexing and Cluster analysis.

He most often published in these fields:

  • Artificial intelligence (55.14%)
  • Machine learning (32.97%)
  • World Wide Web (15.14%)

What were the highlights of his more recent work (between 2001-2020)?

  • World Wide Web (15.14%)
  • Data mining (9.19%)
  • Artificial intelligence (55.14%)

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

Michael J. Pazzani spends much of his time researching World Wide Web, Data mining, Artificial intelligence, Machine learning and Newspaper. His study in World Wide Web is interdisciplinary in nature, drawing from both Variety and User interface. The Data mining study combines topics in areas such as Wearable technology, Search engine indexing and Cluster analysis.

His Search engine indexing study incorporates themes from Time-series segmentation, Time series, Representation, Association rule learning and Euclidean distance. His study in the field of Bayesian probability, Conditional independence and Bayesian inference is also linked to topics like Ask price and Class. His work on Naive Bayes classifier, Stability and Online machine learning is typically connected to Proactive learning as part of general Machine learning study, connecting several disciplines of science.

Between 2001 and 2020, his most popular works were:

  • Content-based recommendation systems (1623 citations)
  • Segmenting Time Series: A Survey and Novel Approach (414 citations)
  • Locally adaptive dimensionality reduction for indexing large time series databases (316 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include World Wide Web, Series, Variety, Cluster analysis and Search engine indexing. Michael J. Pazzani has researched World Wide Web in several fields, including Context and User modeling. His Series research integrates issues from Algorithm, Dynamic time warping, Representation and Time series.

As part of one scientific family, Michael J. Pazzani deals mainly with the area of Variety, narrowing it down to issues related to the Web page, and often Recommender system. The concepts of his Search engine indexing study are interwoven with issues in Nearest neighbor search, Dimensionality reduction, Distance measures and Euclidean distance. The various areas that Michael J. Pazzani examines in his Data mining study include Property and Machine 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

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Pedro Domingos;Michael Pazzani.
Machine Learning (1997)

3839 Citations

Content-based recommendation systems

Michael J. Pazzani;Daniel Billsus.
The adaptive web (2007)

3055 Citations

A Framework for Collaborative, Content-Based and Demographic Filtering

Michael J. Pazzani.
Artificial Intelligence Review (1999)

2179 Citations

Learning and Revising User Profiles: The Identification ofInteresting Web Sites

Michael Pazzani;Daniel Billsus.
Machine Learning (1997)

1894 Citations

Dimensionality reduction for fast similarity search in large time series databases

Eamonn J. Keogh;Kaushik Chakrabarti;Michael J. Pazzani;Sharad Mehrotra.
Knowledge and Information Systems (2001)

1700 Citations

Learning Collaborative Information Filters

Daniel Billsus;Michael J. Pazzani.
international conference on machine learning (1998)

1696 Citations

An online algorithm for segmenting time series

E. Keogh;S. Chu;D. Hart;M. Pazzani.
international conference on data mining (2001)

1300 Citations

Syskill & webert: Identifying interesting web sites

Michael Pazzani;Jack Muramatsu;Daniel Billsus.
national conference on artificial intelligence (1996)

1222 Citations

Derivative Dynamic Time Warping.

Eamonn J. Keogh;Michael J. Pazzani.
siam international conference on data mining (2001)

1144 Citations

Locally adaptive dimensionality reduction for indexing large time series databases

Eamonn Keogh;Kaushik Chakrabarti;Michael Pazzani;Sharad Mehrotra.
international conference on management of data (2001)

1079 Citations

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