PAC Learning and The VC Dimension

Jul 15, 2012

290 likes | 630 Views

Share Presentation

Embed Code

Link

risa

Download Presentation

PAC Learning and The VC Dimension

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

1. PAC Learning and The VC Dimension

Dimension Data Learning Solutions

Dimension Data Learning Solutions. Cisco Systems Career Certifications. Introductions. Dimension Data International IT Services Company Cisco Gold certified partner on five continents www.didata.com Dimension Data Learning Solutions (DDLS-US) Formerly CCTI

906 views • 35 slides

Bayesian Learning VC Dimension

Bayesian Learning VC Dimension . Jahwan Kim 2000. 5. 24 AIPR Lab. CSD., KAIST. Contents. Bayesian learning General idea, & an example Parametric vs. nonparametric statistical inference Model capacity and generalizability Further readings. Bayesian learning.

342 views • 17 slides

The Why Dimension Opening Frontiers for Digital Learning

The Why Dimension Opening Frontiers for Digital Learning. PhD by Published Papers. Jon Mason PhD Final Seminar QUT, September 26, 2013. The Elevator Pitch. It’s all about one word: why Why? It is fundamental to learning & making sense of things

502 views • 36 slides

Employability- Learning to Work: The Scottish dimension

Employability- Learning to Work: The Scottish dimension. Alastair Robertson, Higher Education Academy Presentation at: “The National Agenda for Employability: Implications for HE policy and practice” 28 June 2007. Outline. Employability in Scottish HE- a recent history

307 views • 21 slides

Dimension of Learning: Extend and Refine Knowledge

Dimension 3. Dimension of Learning: Extend and Refine Knowledge. Presented by Denise Tarlinton Quality Teaching Conference Kurwongbah State School 13 August 2003. How’s your thinking? If the day before the day before yesterday was Tuesday, what is the day after the day after tomorrow?.

810 views • 37 slides

Dimension of Learning 4

Dimension of Learning 4 . NAPLAN: How does systematic assessment and reporting impact on student success?. SYSTEMS ANALYSIS. NAPLAN ( National Assessment Program – Literacy & Numeracy ). Assessment Data is gained from student achievement to inform the curriculum’s ongoing implementation.

490 views • 28 slides

VC v. VCG: Inapproximability of Combinatorial Auctions via Generalizations of the VC Dimension

VC v. VCG: Inapproximability of Combinatorial Auctions via Generalizations of the VC Dimension. Michael Schapira Yale University and UC Berkeley. Joint work with Christos Papadimitriou and Yaron Singer (2008) and Elchanan Mossel, Christos Papadimitriou and Yaron Singer (2009).

359 views • 26 slides

Teaching Dimension and the Complexity of Active Learning

Teaching Dimension and the Complexity of Active Learning. Steve Hanneke Machine Learning Department Carnegie Mellon University shanneke@cs.cmu.edu. Passive Learning. Data Source. Expert / Oracle. Learning Algorithm. Raw Unlabeled Data. Labeled examples.

342 views • 19 slides

VC Dimension – definition and impossibility result

VC Dimension – definition and impossibility result. Lecturer: Yishay Mansour Eran Nir and Ido Trivizki. VC Dimension – Lecture Overview. PAC Model – Review VC dimension – motivation Definitions Some examples of geometric concepts Sample size lower bounds More examples.

464 views • 25 slides

Resource bounded dimension and learning

Resource bounded dimension and learning. Joint work with Ricard Gavaldà, María López-Valdés, and Vinodchandran N. Variyam. Elvira Mayordomo, U. Zaragoza CIRM, 2009. Contents. Resource-bounded dimension Learning models A few results on the size of learnable classes Consequences.

442 views • 32 slides

E-learning and the International Dimension of Higher Education

E-learning and the International Dimension of Higher Education . Ria Jacobi, SURF Karen Jager, Erasmus University Rotterdam Henk Frencken, Leiden University The Netherlands. Practice example. Different motives. Lessons learnt (1). Motives for promoting international

324 views • 21 slides

Sketched Derivation of error bound using VC-dimension (1)

Sketched Derivation of error bound using VC-dimension (1). Bound our usual PAC expression by the probability that an algorithm has 0 error on the training examples S but high error on a second random sample , using a Chernoff bound:.

456 views • 20 slides

PAC Learning VC Dimension (Most slides by courtesy of Prof . Carlos Guestrin )

PAC Learning VC Dimension (Most slides by courtesy of Prof . Carlos Guestrin ). 10-701/15-781 Machine Learning Fall 2010 Recitation by Leman Akoglu 11/4/10. We only care about the maximum number of points that can be classified exactly!. Other examples of VC dimension.

438 views • 22 slides

The Vac-Pac

The Vac-Pac. Orange Team. Overview. The challenge Need for better vaccine storage during travel The solution Vac-Pac description Performance Test data Comparison to competitors Plan for future Business Plan Financials. Background.

386 views • 22 slides

Objectives: Error Bounds Complexity Theory PAC Learning PAC Bound Margin Classifiers

LECTURE 27: PAC-BAYES BOUND. Objectives: Error Bounds Complexity Theory PAC Learning PAC Bound Margin Classifiers Resources: D.M.: Simplified PAC-Bayes J.L.: PAC-Bayes and Margins M.S.: PAC-Bayes and Gaussians Wiki: Rademacher Complexity. Audio:. URL:. Review. Generalized Risk:

305 views • 11 slides

Computational Learning Theory PAC IID VC Dimension SVM

Computational Learning Theory PAC IID VC Dimension SVM. Marius Bulacu. Kunstmatige Intelligentie / RuG. The Problem. Why does learning work? How do we know that the learned hypothesis h is close to the target function f if we do not know what f is?. answer provided by

265 views • 13 slides

Machine Learning Lecture 5: Theory I – PAC Learning

Machine Learning Lecture 5: Theory I – PAC Learning. Moshe Koppel Slides adapted from Tom Mitchell. To shatter n examples, we need 2 n hypotheses (since there are that many dichotomies. So the number of examples we can shatter with |H| hypotheses < log |H|.

431 views • 24 slides

PAC Learning

PAC Learning. 8/5/2005. purpose. Effort to understand negative selection algorithm from totally different aspects Statistics Machine learning What is machine learning, in a very informal way?

1.01k views • 21 slides

Probably Approximately Correct Learning (PAC)

Probably Approximately Correct Learning (PAC). Leslie G. Valiant. A Theory of the Learnable. Comm. ACM (1984) 1134-1142. Recall: Bayesian learning. Create a model based on some parameters Assume some prior distribution on those parameters Learning problem

643 views • 60 slides

VC Dimension – definition and impossibility result

305 views • 25 slides

The probably approximately correct (PAC) learning model

The probably approximately correct (PAC) learning model. PAC learnability. A formal, mathematical model of learnability It origins from a paper by Valiant (“A theory of the learnable” , 1984 ) It is very theoretical Has only very few results that are usable in practice

276 views • 25 slides

PAC-Learning and Reconstruction of Quantum States

PAC-Learning and Reconstruction of Quantum States. . Scott Aaronson (University of Texas at Austin) COLT’2017, Amsterdam. What Is A Quantum (Pure) State?. A unit vector of complex numbers.

274 views • 25 slides

More Related