'Incremental learning' diaporamas de présentation

Machine Learning and ILP for Multi-Agent Systems

Machine Learning and ILP for Multi-Agent Systems. Daniel Kudenko & Dimitar Kazakov Department of Computer Science University of York, UK. ACAI-01, Prague, July 2001. Why Learning Agents?. Agent designers are not able to foresee all situations that the agent will encounter.

1.32k views • 113 slides

The Virtue of Reward:

The Virtue of Reward:. Performance, Reinforcement and Discovery in Case-Based Reasoning. Derek Bridge University College Cork Ireland. International Conference on Case-Based Reasoning 2005 Invited talk. Overview. Motivation Reinforcement Learning Case-Based Classifier Systems

576 views • 49 slides

Stochastic Optimization with Learning For Complex Problems

Stochastic Optimization with Learning For Complex Problems. Thrust/Subthrust: Design Synthesis/Logic Synthesis Student: Lixin Su (June, 1999) Advisor: Prof. A. Richard Newton Mentor: Dr. Leon Stok, IBM (http://www-cad.eecs.berkeley.edu/~newton/presentations/SRC3_98).

555 views • 46 slides

Multi-Group Tracking with Adaptive Target Model

Multi-Group Tracking with Adaptive Target Model. PhD thesis proposal. Loris Bazzani, PhD student (XXIV cycle) University of Verona Department of Computer Science. Objectives. Compare existing Multi-Target Tracking methods, studying the sampling technique .

506 views • 35 slides

Automated Parameter Setting Based on Runtime Prediction:

Automated Parameter Setting Based on Runtime Prediction:. Towards an Instance-Aware Problem Solver. Frank Hutter, Univ. of British Columbia, Vancouver, Canada Youssef Hamadi, Microsoft Research, Cambridge, UK. Motivation(1): Why automated parameter setting ?.

477 views • 35 slides

Automated Parameter Setting Based on Runtime Prediction:

Automated Parameter Setting Based on Runtime Prediction:. Towards an Instance-Aware Problem Solver. Frank Hutter, Univ. of British Columbia, Vancouver, Canada Youssef Hamadi, Microsoft Research, Cambridge, UK. Motivation(1): Why automated parameter setting ?.

440 views • 35 slides

Automated Parameter Setting Based on Runtime Prediction:

Automated Parameter Setting Based on Runtime Prediction:. Towards an Instance-Aware Problem Solver. Frank Hutter, Univ. of British Columbia, Vancouver, Canada Youssef Hamadi, Microsoft Research, Cambridge, UK. Motivation(1): Why automated parameter setting ?.

353 views • 35 slides

Application of Genetic Programming for Multicategory Pattern Classification

Application of Genetic Programming for Multicategory Pattern Classification. Kishore, J.K., Patnaik, L.M., Mani, V., Agrawal, V.K., IEEE Transactions on Evolutionary Computation , Vol. 4, No. 3, pp. 242-258, 2000 ? ? ?. Introduction. Multicategory Pattern Classification

403 views • 30 slides

Incremental Learning of System Log Formats

Incremental Learning of System Log Formats. October 2009. Overview. A complex system emits a variety of log files describing its behavior. Declarative data descriptions may describe the format of log files. Log Files. Incremental Learning System. Existing Descriptions.

397 views • 24 slides

New Approaches to DFA learning

New Approaches to DFA learning. Cristina Bibire Research Group on Mathematical Linguistics, Rovira i Virgili University Pl. Imperial Tarraco 1, 43005, Tarragona, Spain E-mail: cristina.bibire@estudiants.urv.es. Introduction Learning from queries Learning from corrections

370 views • 23 slides

A Novel Local Patch Framework for Fixing Supervised Learning Models

A Novel Local Patch Framework for Fixing Supervised Learning Models. Yilei Wang 1 , Bingzheng Wei 2 , Jun Yan 2 , Yang Hu 2 , Zhi-Hong Deng 1 , Zheng Chen 2. 1 Peking University 2 Microsoft Research Asia. Outline. Motivation & Background Problem Definition & Algorithm Overview

342 views • 23 slides

Visual Learning with Navigation as an Example

Visual Learning with Navigation as an Example . Dr Juyang Weeng Dr Shaoyun Chen Michigan Sate University. Model Based Methods. PROS Efficient for predictable cases Easier to understand Computationally inexpensive CONS Non generic Not able to deal with every possible case

292 views • 21 slides

Incremental Linear Discriminative Analysis (LDA)

Incremental Linear Discriminative Analysis (LDA). Presenter : Seung Hwan, Bae. Contents. Introduction Sufficient Spanning Set Incremental LDA Updating the total scatter matrix Updating the between scatter matrix Updating the projection matrix Conclusion.

358 views • 19 slides

An Intensive Approach to Extensive Learning

345 views • 13 slides