'Statistical learning' diaporamas de présentation

STATISTICAL LEARNING METHODS FOR MICROSTRUCTURES

STATISTICAL LEARNING METHODS FOR MICROSTRUCTURES. Veera Sundararaghavan and Prof. Nicholas Zabaras. Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H. T. Rhodes Hall Cornell University Ithaca, NY 14853-3801

1.35k views • 116 slides

1.21k views • 108 slides

Prof. Nicholas Zabaras

STATISTICAL LEARNING TECHNIQUES FOR MICROSTRUCTURE CLASSIFICATION AND REPRESENTATION WITH APPLICATIONS IN MATERIALS DESIGN. Prof. Nicholas Zabaras. Materials Process Design and Control Laboratory Sibley School of Mechanical and Aerospace Engineering 188 Frank H. T. Rhodes Hall

829 views • 66 slides

Statistical Learning in Infants (and bigger folks)

Statistical Learning in Infants (and bigger folks). Statistical Learning. Neural network models emphasize the value of statistical information in language What information can be extracted from this? Is this sufficient to account for human performance?

733 views • 56 slides

661 views • 56 slides

Psych 156A/ Ling 150: Acquisition of Language II

Psych 156A/ Ling 150: Acquisition of Language II. Lecture 16 Learning Language Structure. Announcements. Please pick up HW3 Work on structure review questions For those with 88%+ in the class: Let me know if you will be writing a final paper instead of taking the final exam on June 8.

678 views • 47 slides

Psych156A/Ling150: Psychology of Language Learning

Psych156A/Ling150: Psychology of Language Learning. Lecture 19 Learning Structure with Parameters. Announcements. Next class: Review session for final - Review homework and quiz questions, come in with questions to go over

532 views • 44 slides

Security problems of your keyboard Authentication Compromising emanations consist of electrical, mechanical, or acoustic

Security problems of your keyboard Authentication Compromising emanations consist of electrical, mechanical, or acoustical Supply chain attack (Bluetooth, SD card) Power usage. Key stroke biometrics with number-pad input (DSN 2010) 28 users typed the same 10 digit number

312 views • 31 slides

Different Features

Different Features. Glasses vs. No Glasses. Beard vs. No Beard. Beard Distinction. Ghodsi et, al 2007. Glasses Distinction. Ghodsi et, al 2007. Multiple-Attribute Metric. Ghodsi et, al 2007. Embedding of sparse music similarity graph. Platt, 2004. Reinforcement learning.

397 views • 31 slides

ITCS 3153 Artificial Intelligence

ITCS 3153 Artificial Intelligence. Lecture 17 First-Order Logic Chapter 9. Inference in first-order logic. Our goal is to prove that KB entails a fact, a We use logical inference Forward chaining Backward chaining Resolution

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365 views • 23 slides

INTUITIVE DECISION MAKING IN IMMERSIVE ENVIRONMENTS Robert Patterson, Ph.D.

INTUITIVE DECISION MAKING IN IMMERSIVE ENVIRONMENTS Robert Patterson, Ph.D. Distribution A: Approved for public release; distribution unlimited. (Approval given by 88 ABW/2010-0064 on January 7, 2010).

433 views • 22 slides

293 views • 20 slides

Statistical Learning in Astrophysics

338 views • 19 slides

The Elements of Statistical Learning

The Elements of Statistical Learning. http://www-stat.stanford.edu/~ tibs/ElemStatLearn/download.html http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf. Elements of Statistical Learning Printing 10 with corrections. Contents 1. Introduction

1.07k views • 17 slides

Semantics and Context in Natural Language Processing (NLP)

Semantics and Context in Natural Language Processing (NLP). Ari Rappoport The Hebrew University. Form vs. Meaning. Solid NLP progress with statistical learning OK for classification, search, prediction Prediction: language models in speech to text

361 views • 14 slides

Introduction to a language and environment for statistical computing and graphics.

A tutorial by Eric Gilliland and Matt Pocernich March 29th, 2006. Introduction to a language and environment for statistical computing and graphics. Origins of R. Influenced by two existing languages: Becker, Chambers & Wilks' S (ATT) Sussman's Scheme

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Conclusions

Conclusions. overview and survey from a logical / ILP perspective Distinction between Model-based: BN, PLPs, PRMs,BLPs,... vs. proof-based: SCFGs, SLPs, PRISM, ... Learning Settings: Learning from interpretations: PLPs,PRMs,BLPs Learning from entailment: SLPs, PRISM

172 views • 8 slides

Welcome to the Winter 2004 ROC Retreat

Welcome to the Winter 2004 ROC Retreat. Armando Fox and David Patterson. About ROC Retreats. Purpose of semi-annual retreats Progress reports/talks from academia and industry Exposure/feedback on new ideas or work in progress Brainstorming in immersive atmosphere

177 views • 7 slides

Micro-Scale Modeling combined with Statistical Learning for

Short-term forecasting is usually classified into two groups: the first approach uses physical model in order to compute the downscaling, whereas the second group relies on statistical learning. We propose a new strategy based on both approaches: a micro-scale CFD model coupled with an artificial neural network correction. Selection of the optimal neural network is achieved through a genetic algorithm. This solution is tested on a real case, which leads to a relative RMSE improvement of 17%.

90 views • 1 slides