Person: Michael J. Pazzani

DBLP: Pazzani:Michael_J=

Contributed to:

2011

2010

2006

2001

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1999

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1989

1988

Wrote 27 papers:

KDD-2011-MesterharmP #algorithm #learning #online #using

Active learning using on-line algorithms (CM, MJP), pp. 850–858.

KDD-2010-GeXTXGP #energy #mobile #recommendation

An energy-efficient mobile recommender system (YG, HX, AT, KX, MG, MJP), pp. 899–908.

KDD-2006-HettichP #lessons learnt #mining

Mining for proposal reviewers: lessons learned at the national science foundation (SH, MJP), pp. 862–871.

KDD-2001-KeoghCP #approach #database #named #scalability

Ensemble-index: a new approach to indexing large databases (EJK, SC, MJP), pp. 117–125.

ICML-2000-BayP #difference

Characterizing Model Erros and Differences (SDB, MJP), pp. 49–56.

KDD-2000-KeoghP #scalability

Scaling up dynamic time warping for datamining applications (EJK, MJP), pp. 285–289.

KDD-1999-BayP #category theory #data mining #detection #mining #set

Detecting Change in Categorical Data: Mining Contrast Sets (SDB, MJP), pp. 302–306.

SIGIR-1999-KeoghP #feedback #retrieval

Relevance Feedback Retrieval of Time Series Data (EJK, MJP), pp. 183–190.

ICML-1998-BillsusP #collaboration #learning

Learning Collaborative Information Filters (DB, MJP), pp. 46–54.

KDD-1998-KeoghP #classification #clustering #feedback #performance #representation

An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback (EJK, MJP), pp. 239–243.

KDD-1997-PazzaniMS #learning

Beyond Concise and Colorful: Learning Intelligible Rules (MJP, SM, WRS), pp. 235–238.

ICML-1996-DomingosP #classification #independence

Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier (PMD, MJP), pp. 105–112.

ICML-1995-BrunkP #bias #semantics

A Lexical Based Semantic Bias for Theory Revision (CB, MJP), pp. 81–89.

ICML-1995-YamazakiPM #ambiguity #learning #natural language

Learning Hierarchies from Ambiguous Natural Language Data (TY, MJP, CJM), pp. 575–583.

KDD-1995-Pazzani #approach #classification

An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers (MJP), pp. 228–233.

ICML-1994-MurphyP

Revision of Production System Rule-Bases (PMM, MJP), pp. 199–207.

ICML-1994-PazzaniMMAHB #classification

Reducing Misclassification Costs (MJP, CJM, PMM, KMA, TH, CB), pp. 217–225.

LOPSTR-1994-SemeraroEMBP #case study #learning #logic #source code

Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL (GS, FE, DM, CB, MJP), pp. 183–198.

ML-1992-HirschbergP #analysis #concept #learning

Average Case Analysis of Learning κ-CNF Concepts (DSH, MJP), pp. 206–211.

ML-1991-BrunkP #algorithm #concept #learning #relational

An Investigation of Noise-Tolerant Relational Concept Learning Algorithms (CB, MJP), pp. 389–393.

ML-1991-MurphyP #induction

Constructive Induction of M-of-N Terms (PMM, MJP), pp. 183–187.

ML-1991-PazzaniBS #approach #concept #learning #relational

A Knowledge-intensive Approach to Learning Relational Concepts (MJP, CB, GS), pp. 432–436.

ML-1991-SilversteinP #induction #learning #relational

Relational Clichés: Constraining Induction During Relational Learning (GS, MJP), pp. 203–207.

ML-1990-PazzaniS #algorithm #analysis #learning

Average Case Analysis of Conjunctive Learning Algorithms (MJP, WS), pp. 339–347.

ML-1989-Pazzani #learning

Explanation-Based Learning with Week Domain Theories (MJP), pp. 72–74.

ML-1989-SarrettP #algorithm #empirical #learning

One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning (WS, MJP), pp. 26–28.

ML-1988-Pazzani #learning

Integrated Learning with Incorrect and Incomplete Theories (MJP), pp. 291–297.