'Instance based' presentation slideshows

Machine Learning in Practice Lecture 21

847 views • 84 slides

Instance based and Bayesian learning

Instance based and Bayesian learning. Kurt Driessens with slide ideas from a.o . Hendrik Blockeel , Pedro Domingos , David Page, Tom Dietterich and Eamon Keogh. Overview. Nearest neighbor methods Similarity Problems: dimensionality of data, efficiency, etc. Solutions:

720 views • 62 slides

540 views • 48 slides

Ontology Mapping in Pervasive Computing Environment

Ontology Mapping in Pervasive Computing Environment. C.Y. Kong, C.L. Wang, F.C.M. Lau The University of Hong Kong. Outline. Introduction Literature review Proposed design Evaluation Conclusion and Future works. Pervasive Computing.

483 views • 35 slides

Knowledge Representation

Knowledge Representation. Outline: Output - Knowledge representation. Decision tables Decision trees Decision rules Rules involving relations Instance-based representation Prototypes, Clusters. witten&eibe. Output: representing structural patterns.

343 views • 34 slides

Instance-based Learning Algorithms

Instance-based Learning Algorithms. Presented by Yan T. Yang. Agenda. Background what is instance-based learning? Two simple algorithms Extensions [Aha, 1994]: F eedback algorithm Noise reduction Irrelevant attribute elimination Novel attribute adoption. Learning Paradigms.

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Knowledge Representation

Knowledge Representation. Outline: Output - Knowledge representation. Decision tables Decision trees Decision rules Rules involving relations Instance-based representation Prototypes, Clusters. witten&eibe. Output: representing structural patterns.

415 views • 29 slides

Knowledge Representation

Knowledge Representation. Outline: Output - Knowledge representation. Decision tables Decision trees Decision rules Rules involving relations Instance-based representation Prototypes, Clusters. witten&eibe. Output: representing structural patterns.

295 views • 29 slides

Case-Based Reasoning

446 views • 29 slides

Ontology Alignment

418 views • 28 slides

HANOLISTIC: a h ierarchical automatic image an notation system using h olistic approach

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HANOLISTIC: a h ierarchical automatic image an notation system using h olistic approach

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Short Introduction to Machine Learning Instructor: Rada Mihalcea

Short Introduction to Machine Learning Instructor: Rada Mihalcea. Learning?. What can we learn from here? If Sky=Sunny and Air Temperature = Warm ? Enjoy Sport = Yes If Sky=Sunny ? Enjoy Sport = Yes If Air Temperature = Warm ? Enjoy Sport = Yes

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CSC 196k Semester Project: Instance Based Learning

CSC 196k Semester Project: Instance Based Learning. Weka Assignment 2 Glynis Hawley. Agenda. Background: Instance-based Learning Project Requirements Data Progress Conclusions References. Background: Instance Based Learning.

299 views • 17 slides

Schema matching for Database Systems

Schema matching for Database Systems. Bhavik Doshi Chair: Prof. Rajendra K. Raj Reader: Dr. Carol Romanowski Department of Computer Science Rochester Institute of Technology. EMPID. Name. Salary. EMPID. Name. Salary. has. Employee. Position-type. Employee. SSN. Position. SSN.

124 views • 12 slides

Schema matching for Database Systems

Schema matching for Database Systems. Bhavik Doshi Chair: Prof. Rajendra K. Raj Reader: Dr. Carol Romanowski Department of Computer Science Rochester Institute of Technology. EMPID. Name. Salary. EMPID. Name. Salary. has. Employee. Position-type. Employee. SSN. Position. SSN.

130 views • 12 slides