Special Sessions

We are pleased to announce the following three special sessions to be held during the 6th Int'l Symposium on Artificial Intelligence and Mathematics, Jan. 5-7, 2000 in Ft. Lauderdale, Florida.

Mathematical Aspects of Knowledge Discovery and Data Analysis

Organized by
Prof. Ronen Feldman
Prof. Martin Golumbic
Prof. Peter Hammer
Prof. Alexander Kogan

Satisfiability and Theorem Proving

Organized by
Prof. Endre Boros

Knowledge exploration for predictive toxicity of chemicals

Organized by
Prof. Giuseppina Gini

Further information on the Symposium, registration, hotel, schedule, web-proceedings, etc. can be found at http://rutcor.rutgers.edu/~amai

Titles of Lectures from the special sessions

Mathematical Aspects of Knowledge Discovery and Data Analysis

Pareto-Optimal Patterns in Logical Analysis of Data

Peter L. Hammer
Alexander Kogan
Bruno Simeone

Identification of Frequent Sets and Association Rules

Jan Cor Bioch

Average Case Performance of the Apriori Algorithm

Paul Purdom

Generating all ``good'' patterns in polynomial expected time

Endre Boros
Lijie Shi RUTCOR, Rutgers University and
Mutsunori Yagiura Applied Mathematics and Physics Department, Graduate School of Engineering, Kyoto University

Algorithms for Massive Data Streams

Martin Strauss

Multiple Randomized Classifiers: Why boosting and randomized forests really work

Yali Amit
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SATISFIABLILTY AND THEOREM PROVING

A parallel approach to resolution-refutation proofs.

Monroe Newborn

Practical Heuristics for Solving Problems for Serializable Subgoals

Mohammed Almulla
A. El-Sheikh

Qualitative Theorem Proving in Linear Constraints

Vijay Chandru
Catherine Lassez
Jean-Louis Lassez

Some results about the probabilistic SAT problem

Daniele Pretolani
Kim Allan Andersen

Solving some SAT instances by cutting planes

Ming Ouyang

Semi-definite relaxations of 2+p-SAT problems ; another phase-transition?

Hans van Maaren
Lucie Aarts

The mechanics of upper and lower bound arguments related to the probabilistic complexity of resolution-based algorithms on random k-SAT formulas

John Franco
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Knowledge Exploration for Predictive Toxicity of Chemicals

Prediction of ecotoxicity of pesticides: comparison of multivariate analysis, neural networks, and classifiers

Gini, G., Balestri, M., DEI, Politecnico di Milano, Italy
Benfenati, E., Pelagatti, S., Istituto Mario Negri, Milano, Italy

Knowledge Exploration for Toxicity Prediction by Using Genetic Optimized B-Spline Networks,

Adolf Grauel
Ingo Renners
Lars A. Ludwig

Computational Intelligence Methods Aid the Design of Safe Chemicals

Les M. Sztandera
Charles Bock
Mendel Trachtman

Structure-Activity Relationship Models: Using the Results of Model Ensembles

Nancy B. Sussman