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A Novel Approach to Novelty Detection: Voronoi Tesselation

A Novel Approach to Novelty Detection: Voronoi Tesselation

A Novel Approach to Novelty Detection: Voronoi Tesselation. Jeffrey.D.Scargle@nasa.gov Space Science Division NASA Ames Research Center. Collaborator: Nikunj Oza, NASA-Ames Research Center, IC PureSense, Inc. Machine Learning Seminar. The Basic Ideas Nonparametric Density Estimation

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743 views • 58 slides
Temporal Relationship Among Clusters for Data Streams

Temporal Relationship Among Clusters for Data Streams

Temporal Relationship Among Clusters for Data Streams. Margaret H. Dunham, Michael Hahsler, Doug Raiford Students: Yu Meng , Donya Quick, Jie Huang, Charlie Isaksson, Mallik Kotamarti CSE Department Southern Methodist University Dallas, Texas 75275 mhd@lyle.smu.edu

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561 views • 48 slides
k -Nearest neighbors and decision tree

k -Nearest neighbors and decision tree

k -Nearest neighbors and decision tree. Nonparametric Supervised Learning. Outline. Context of these algorithms K - n earest neighbors ( k -NN) 1-nearest neighbor (1-NN Extension to k -nearest n eighbors Decision Tree Summary. Outline. Context of these algorithms

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659 views • 32 slides
Know Thy Neighbor: An Introduction to Scikit-learn and K-NN

Know Thy Neighbor: An Introduction to Scikit-learn and K-NN

Know Thy Neighbor: An Introduction to Scikit-learn and K-NN. Portia Burton Portland Data Science Group March 25, 2014. What We will Cover Today. 1. Define What is Machine Learning 2. Go Over Scikit-learn 3. Explain k-Nearest Neighbor 4. Demo of Scikit-learn and k-Nearest Neighbor .

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377 views • 20 slides
Part I. Simple random sampling

Part I. Simple random sampling

Part I. Simple random sampling. its story and the link to confidence intervals. Sample of size n = ?. Think of choosing an apartment complex to live in based on the expected utilities fee you would pay. The choices are Spruce Heights and Granada, where each has 90 equal-size units.

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271 views • 13 slides
LECTURE 20: ESTIMATING, COMPARING AND COMBINING CLASSIFIERS

LECTURE 20: ESTIMATING, COMPARING AND COMBINING CLASSIFIERS

LECTURE 20: ESTIMATING, COMPARING AND COMBINING CLASSIFIERS. Objectives: Bagging and Boosting Cross-Validation ML and Bayesian Model Comparison Combining Classifiers

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238 views • 12 slides

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