Complete Distributional Problems, Hard Languages, and Resource-Bounded Measure A. Pavan† Alan L. Selman‡ Department of Computer Science University at Buffalo

Source URL: www.cse.buffalo.edu

• Complexity classes
• NP-complete
• P
• Time complexity
• Reduction
• Many-one reduction
• P versus NP problem
• NP-hard
• Theoretical computer science
• Computational complexity theory
• Applied mathematics

Redundancy in Complete Sets Christian Glaßer∗, A. Pavan†, Alan L. Selman‡, Liyu Zhang§ July 6, 2005 Abstract We show that a set is m-autoreducible if and only if it is m-mitotic. This solves a long standing

Source URL: www.cse.buffalo.edu

• Complexity classes
• Structural complexity theory
• Conjectures
• Mathematical optimization
• P versus NP problem
• PP
• NP
• Polynomial hierarchy
• P
• Theoretical computer science
• Computational complexity theory
• Applied mathematics

Autoreducibility, Mitoticity, and Immunity Christian Glaßer∗, Mitsunori Ogihara†, A. Pavan‡, Alan L. Selman§, Liyu Zhang¶ December 27, 2004 Abstract We show the following results regarding complete sets.

Source URL: www.cse.buffalo.edu

• Complexity classes
• Structural complexity theory
• Mathematical optimization
• Probabilistic complexity theory
• NP
• PP
• Polynomial hierarchy
• PSPACE-complete
• P versus NP problem
• Theoretical computer science
• Computational complexity theory
• Applied mathematics

Bi-Immunity Separates Strong NP-Completeness Notions A. Pavan?1 and Alan L Selman2 1 2

Source URL: www.cse.buffalo.edu

• NP-complete
• NP
• P
• Combinatory logic
• P versus NP problem
• Computational complexity theory
• Theoretical computer science
• Complexity classes
• Applied mathematics

        Gianni  Pavan      [removed]                                                            

Source URL: mammiferimarini.unipv.it

Non-parametric Mixture Models for Clustering Pavan Kumar Mallapragada, Rong Jin and Anil Jain Department of Computer Science and Engineering, Michigan State University, East Lansing, MI[removed]Abstract. Mixture models

Source URL: biometrics.cse.msu.edu

• Multivariate statistics
• Data analysis
• Data mining
• K-means clustering
• Spectral clustering
• Mixture model
• DBSCAN
• Unsupervised learning
• Hierarchical clustering
• Statistics
• Machine learning
• Cluster analysis

Active Query Selection for Semi-supervised Clustering Pavan Kumar Mallapragada, Rong Jin and Anil K. Jain Department of Computer Science and Engineering Michigan State University, East Lansing, MI 48823 {pavanm,rongjin,j

Source URL: dataclustering.cse.msu.edu

• Machine learning
• Constrained clustering
• Cluster analysis
• K-means clustering
• Consensus clustering
• Statistics
• Computational statistics
• Data mining

To appear in the IEEE Transactions on Pattern Analysis and Machine Intelligence. 1 SemiBoost: Boosting for Semi-supervised Learning Pavan Kumar Mallapragada, Student Member, IEEE, Rong Jin, Member, IEEE, Anil K. Jain, F

Source URL: dataclustering.cse.msu.edu

• Learning
• Co-training
• Supervised learning
• Semi-supervised learning
• Margin classifier
• Support vector machine
• Transduction
• Pattern recognition
• Boosting
• Machine learning
• Statistics
• Artificial intelligence

Efficient Multi-label Ranking for Multi-class Learning: Application to Object Recognition Serhat S. Bucak, Pavan Kumar Mallapragada, Rong Jin and Anil K. Jain Michigan State University East Lansing, MI 48824, USA {bucak

Source URL: biometrics.cse.msu.edu

• Statistical classification
• Support vector machine

Updated:03/04/13jjh First Name Pavan Abuzarfar Larry Ewa

Source URL: www.elkhartclinic.com