'Target concept' presentation slideshows

Decision Tree Learning

Decision Tree Learning. ACM Student Chapter, Heritage Institute of Technology 3 rd February, 2012 SIGKDD Presentation by Satarupa Guha Sudipto Banerjee Ashish Baheti. Machine Learning.

1.08k views • 86 slides

Chapter 2: Concept Learning and the General-to-Specific Ordering

569 views • 46 slides

Conceptual Modeling for ETL processes

Conceptual Modeling for ETL processes. Panos Vassiliadis, Alkis Simitsis, Spiros Skiadopoulos {pvassil , asimi , spiros}@dblab . ece . ntua . gr. National Technical University of Athens KDBS Laboratory http://www.dbnet.ece.ntua.gr. General Idea. The problem:

602 views • 41 slides

Selection of Relevant Features and Examples in Machine Learning

Selection of Relevant Features and Examples in Machine Learning. Paper By: Avrim L. Blum Pat Langley. Presented By: Arindam Bhattacharya ( 10305002) Akshat Malu (10305012) Yogesh Kakde (10305039) Tanmay Haldankar ( 10305911). Overview. Introduction

498 views • 36 slides

Problems with Learning

Concept spaces are very large Training sets represent a very small percentage of instances Generalization is not (in general) truth preserving The same training set may allow for different generalizations Heuristics may be necessary to guide search and to constraint the space.

418 views • 29 slides

SNOMED Core Structures

SNOMED Core Structures. NAHLN January 2005 Las Vegas, NV. SNOMED Core. SNOMED Core. Concepts Table Each row in this table represents a medical concept. Descriptions Table Each row in this table specifies a term that can be applied to describe a single clinical concept.

445 views • 26 slides

The Principle of Presence:

The Principle of Presence:. A Heuristic for Growing Knowledge Structured Neural Networks Laurent Orseau, INSA/IRISA, Rennes, France. Neural Networks. Efficient at learning single problems Fully connected Convergence in W 3 Lifelong learning: Specific cases can be important

306 views • 21 slides

Real Estate Strategies for Non-Profit-Organisations (NPOs)

344 views • 20 slides

Concept Learning and Version Spaces

478 views • 17 slides

Machine Learning Chapter 11. Analytical Learning

Machine Learning Chapter 11. Analytical Learning. Tom M. Mitchell. Outline. Two formulations for learning: Inductive and Analytical Perfect domain theories and Prolog-EBG. A Positive Example. The Inductive Generalization Problem. Given: Instances Hypotheses Target Concept

1.51k views • 13 slides