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Exploring Instance-Based Learning: K-Nearest Neighbors and Collaborative Filtering

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This preview delves into the principles of Instance-Based Learning (IBL), focusing on the K-nearest neighbor algorithm and its various applications. We explore how IBL methods utilize training instances to make predictions and decisions. Additionally, the preview highlights other forms of IBL and their significance in collaborative filtering, showcasing their effectiveness in recommendation systems and data analysis. Understanding these concepts is crucial for leveraging IBL in machine learning and artificial intelligence.

Exploring Instance-Based Learning: K-Nearest Neighbors and Collaborative Filtering

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Presentation Transcript

Instance-Based Learning
Preview • K-nearest neighbor • Other forms of IBL • Collaborative filtering

Instance Based Learning

Instance Based Learning. IB1 and IBK Find in text Early approach. 1- Nearest Neighbor. Basic distance function between attribute-values If real, the absolute value If nominal, d(v1,v2) = 1 if v1 =v2, else 0.

345 views • 16 slides

Instance-Based Learning

Instance-Based Learning. By Dong Xu State Key Lab of CAD&amp;CG, ZJU. Overview. Learning Phase Simply store the presented training examples. Query Phase Retrieve similar related instances. Construct local approximation. Report the function value at query point.

406 views • 27 slides

Instance Based Learning

Instance Based Learning . k -Nearest Neighbor Locally weighted regression Radial basis functions Case-based reasoning Lazy and eager learning. Instance-Based Learning. When to Consider Nearest Neighbor. Instance map to points in R n Less than 20 attributes per instance

862 views • 20 slides

Instance Based Learning

Instance Based Learning. Instance Based Learning. Classify based on local similarity Ranges from simple nearest neighbor to case-based and analogical reasoning Use local information near the current query instance to decide the classification of that instance

375 views • 21 slides

Instance Based Learning

Instance Based Learning. IB1 and IBK Small section in chapter 20. 1- Nearest Neighbor. Basic distance function between attribute-values If real, the absolute value If nominal, d(v1,v2) = 1 if v1 =v2, else 0. Distance between 2 instances is square root of sum of square.

284 views • 13 slides

Instance Based Learning

Instance Based Learning. Classify based on local similarity Ranges from simple nearest neighbor to case-based and analogical reasoning Use local information near the current query instance to decide the classification of that instance

406 views • 25 slides

Instance Based Learning

Instance Based Learning. Ata Kaban The University of Birmingham. Today we learn: K-Nearest Neighbours Case-based reasoning Lazy and eager learning. Instance-based learning. One way of solving tasks of approximating discrete or real valued target functions

477 views • 29 slides

Instance Based Learning

Instance Based Learning. Ata Kaban The University of Birmingham. Today we learn: K-Nearest Neighbours Locally weighted regression Case-based reasoning Lazy and eager learning. Instance-based learning. One way of solving tasks of approximating discrete or real valued target functions

619 views • 30 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:

743 views • 62 slides

Chapter Eight Instance Based Learning

Chapter Eight Instance Based Learning. Machine Learning Tom M. Mitchell. Outline. Introduction K-Nearest Neighbor Locally Weighted Regression Radial Basis Functions Case-Based Reasoning Lazy and Eager Learning. What Is Instance Based Learning.

2.61k views • 33 slides

Instance Based Learning

Instance Based Learning. Introduction k-nearest neighbor Locally weighted regression Radial Basis Functions Case-Based Reasoning Summary. Introduction. Instance-based learning divides into two simple steps: 1. Store all examples in the training set.

375 views • 16 slides

Classification using instance-based learning

Classification using instance-based learning. Classification using instance-based learning. Introduction (lazy vs. eager learning) Notion of similarity (proximity, inverse of distance) k-Nearest Neighbor (K-NN) method Distance-weighted K-NN Axis stretching

402 views • 19 slides

Instance based learning

Instance based learning. 基于实例的学习. 上海交通大学. 摘自 T. Mitchell Machine learning. Topics. Introduction k-Nearest Neighbor Learning (kNN) Locally Weighted Regression(LWR) Radial Basis Function (RBF) IBL--NNet Case-Based Reasoning (CBR) Conclusion. IBL: Basic Idea. Key idea:

705 views • 48 slides

Instance-Based Learning

Instance-Based Learning. Content. Motivation Eager Learning Lazy Learning Instance-Based Learning k-Nearest Neighbour Learning (kNN) Distance-Weighted k-NN Locally Weighted Regression (LWR) Case-Based Reasoning (CBR) Summary. Motivation: Eager Learning.

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4. Instance-Based Learning

4. Instance-Based Learning. 4.1 Introduction Instance-Based Learning: Local approximation to the target function that applies in the neighborhood of the query instance Cost of classifying new instances can be high: Nearly all computations take place at classification time

306 views • 18 slides

Instance-Based Learning

Instance-Based Learning. Evgueni Smirnov. Overview. Instance-Based Learning Comparison of Eager and Instance-Based Learning Instance Distances for Instance-Based Learning Nearest Neighbor (NN) Algorithm Advantages and Disadvantages of the NN algorithm

419 views • 25 slides

Instance Based Learning

Instance Based Learning. Nearest Neighbor. Remember all your data When someone asks a question Find the nearest old data point Return the answer associated with it In order to say what point is nearest, we have to define what we mean by &quot;near&quot;.

350 views • 25 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.

480 views • 32 slides

Instance Based Learning

210 views • 16 slides

Instance Based Learning

194 views • 13 slides

Instance Based Learning

Instance Based Learning. Based on Raymond J. Mooney’s slides University of Texas at Austin. Example. Instance-Based Learning.

559 views • 52 slides

Instance-Based &amp; Bayesian Learning

Instance-Based &amp; Bayesian Learning. Chapter 20.1-20.4. Some material adapted from lecture notes by Lise Getoor and Ron Parr. Overview. k-nearest neighbor Naïve Bayes Learning Bayes networks. Instance Based Learning. Decision trees are a kind of model -based learning

269 views • 24 slides

More Related

Exploring Instance-Based Learning: K-Nearest Neighbors and Collaborative Filtering

Exploring Instance-Based Learning: K-Nearest Neighbors and Collaborative Filtering

Presentation Transcript

Instance Based Learning

Instance-Based Learning

Instance Based Learning

Instance Based Learning

Instance Based Learning

Instance Based Learning

Instance Based Learning

Instance Based Learning

Instance based and Bayesian learning

Chapter Eight Instance Based Learning

Instance Based Learning

Classification using instance-based learning

Instance based learning

Instance-Based Learning

4. Instance-Based Learning

Instance-Based Learning

Instance Based Learning

Instance-based Learning Algorithms

Instance Based Learning

Instance Based Learning

Instance Based Learning

Instance-Based &amp;amp; Bayesian Learning

Instance-Based & Bayesian Learning