Module-V Chapter 8 Instance Based Learning, Chapter 8: Instance Based Learning 1. Introduction 2....
Transcript of Module-V Chapter 8 Instance Based Learning, Chapter 8: Instance Based Learning 1. Introduction 2....
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15CS73 - Machine Learning Harivinod N
Module-V Chapter 8
Instance Based Learning,
By
Harivinod NVivekananda College of Engineering
Technology, Puttur
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15CS73 - Machine Learning Harivinod N
Module 5 - Outline
Chapter 8: Instance Based Learning
1. Introduction
2. K-nearest neighbor Learning
3. Locally Weighted regression
4. Radial basis functions
5. Case based reasoning
6. Summary
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15CS73 - Machine Learning Harivinod N
Introduction
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15CS73 - Machine Learning Harivinod N
Introduction
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15CS73 - Machine Learning Harivinod N
Module 5 - Outline
Chapter 8: Instance Based Learning
1. Introduction
2. K-nearest neighbor Learning
3. Locally Weighted regression
4. Radial basis functions
5. Case based reasoning
6. Summary
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15CS73 - Machine Learning Harivinod N
K-nearest neighbor learning (For classification and regression)
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15CS73 - Machine Learning Harivinod N
K-nearest neighbor learning
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15CS73 - Machine Learning Harivinod N
KNN Algorithm for classification
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15CS73 - Machine Learning Harivinod N
K-NN Hypothesis Space
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15CS73 - Machine Learning Harivinod N
K-nearest neighbor learning
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15CS73 - Machine Learning Harivinod N
Distance Weighted Nearest Neighbor
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15CS73 - Machine Learning Harivinod N
Remarks on K-NN
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15CS73 - Machine Learning Harivinod N
Module 5 - Outline
Chapter 8: Instance Based Learning
1. Introduction
2. K-nearest neighbor Learning
3. Locally Weighted regression
4. Radial basis functions
5. Case based reasoning
6. Summary
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15CS73 - Machine Learning Harivinod N
Locally Weighted Regression
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15CS73 - Machine Learning Harivinod N
Locally weighted linear regression
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15CS73 - Machine Learning Harivinod N
Locally weighted linear regression
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15CS73 - Machine Learning Harivinod N
Locally weighted linear regression
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15CS73 - Machine Learning Harivinod N
Algorithm
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15CS73 - Machine Learning Harivinod N
Module 5 - Outline
Chapter 8: Instance Based Learning
1. Introduction
2. K-nearest neighbor Learning
3. Locally Weighted regression
4. Radial basis functions
5. Case based reasoning
6. Summary
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15CS73 - Machine Learning Harivinod N
Radial basis function
One of the approach to function approximation
that is closely related to
distance-weighted regression and
artificial neural networks
is learning with radial basis functions
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15CS73 - Machine Learning Harivinod N
Radial basis function ( for regression)
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15CS73 - Machine Learning Harivinod N
Radial basis function
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15CS73 - Machine Learning Harivinod N
RBF NN
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15CS73 - Machine Learning Harivinod N
Module 5 - Outline
Chapter 8: Instance Based Learning
1. Introduction
2. K-nearest neighbor Learning
3. Locally Weighted regression
4. Radial basis functions
5. Case based reasoning
6. Summary
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15CS73 - Machine Learning Harivinod N
Case Based Reasoning
� Instance-based methods such as k-NN, locally weighted regression share three key properties.
1. They are lazy learning methods
They defer the decision of how to generalize beyond the training data until a new query instance is observed.
2. They classify new query instances by analyzing similar instances while ignoring instances that are very different from the query.
3. Third, they represent instances as real-valued points in an n-dimensional Euclidean space.
�Case-based reasoning (CBR) is a learning paradigm based on the first two of these principles, but not the third.
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15CS73 - Machine Learning Harivinod N
Case Based Reasoning
� In CBR, instances are typically represented using more rich symbolic descriptions, and the methods used to retrieve similar instances are correspondingly more elaborate.
• CBR has been applied to problems such as conceptual design of mechanical devices based on a stored library of previous designs (Sycara et al. 1992),
• reasoning about new legal cases based on previous rulings (Ashley 1990),
• solving planning and scheduling problems by reusing and combining portions of previous solutions to similar problems (Veloso 1992).
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15CS73 - Machine Learning Harivinod N
Case Study
�The CADET system (Sycara et al. 1992)
• employs case based reasoning to assist in the conceptual design of simple mechanical devices such as water faucets.
• It uses a library containing approximately 75 previous designs and
• design fragments to suggest conceptual designs to meet the specifications of new design problems.
• Complete Case study - Self study
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15CS73 - Machine Learning Harivinod N
Summary
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