'Hyper plane' presentation slideshows

Support Vector Machines

Support Vector Machines. Text Book Slides. Find a linear hyperplane (decision boundary) that will separate the data. Support Vector Machines. One Possible Solution. Support Vector Machines. Another possible solution. Support Vector Machines. Other possible solutions.

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Machine learning

Machine learning. Learning mode on. Bioinfo is great. Clustering. Clustering (of expression data). UPGMA is one such direct method, receiving as input a distance matrix and giving as output an ultrametric tree. It was suggested by Sokal and Michener (1958). Clustering (of expression data).

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For C = {a}

r 1. r 1. a. r 2. r 2. C. Gvein any function, f:X(A 1 ..A n ) ?R (any range) and any S ?R, we define contour(f,S) = f -1 (S). There is a DUALITY between functions f:X(A 1 ..A n ) ?R and derived attributes given by x.A f = f(x) or f A (x) = x.A

469 views • 45 slides

For C = {a}

r 1. r 1. a. r 2. r 2. C. Gvein any function, f:X(A 1 ..A n ) ?R (any range) and any S ?R, we define contour(f,S) = f -1 (S). There is a DUALITY between functions f:X(A 1 ..A n ) ?R and derived attributes given by x.A f = f(x) or f A (x) = x.A

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Visualization of TV Space

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Visualization of TV Space

381 views • 37 slides

Context Assistance in Media Interpretations

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Support Vector Machines

Support Vector Machines. H. Clara Pong Julie Horrocks 1 , Marianne Van den Heuvel 2 ,Francis Tekpetey 3, B. Anne Croy 4. 1 Mathematics & Statistics, University of Guelph, 2 Biomedical Sciences, University of Guelph, 3 Obstetrics and Gynecology, University of Western Ontario,

407 views • 27 slides

Vorlesung Generalized Linear Regressionmodels

Vorlesung Generalized Linear Regressionmodels. Antonia Rom. Chapter 4 - Modeling of B inary Data Introduction What is important in modeling? Problems, Obstacles 4.1 Maximum Likelihood Estimation What is the ML-estimation? Single Binary Response Grouped Data

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Kernel Properties

Kernel Properties. 2012 Computer Science PhD Showcase 17 February 2012 Roberto Valerio Dr. Ricardo Vilalta Pattern Analysis Lab. Kernel Properties. Agenda Introduction Objective Current work Experiments Conclusions Publications. Introduction. Machine Learning What is it?

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Chapter 6. Threshold Logic

Chapter 6. Threshold Logic. Logic design of sw functions constructed of electronic gates different type of switching element : threshold element. Threshold element can be considered as a generalization of the conventional gates.

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Lecture 17 SVM (I): Basic Formulation

Lecture 17 SVM (I): Basic Formulation. Outline. Linear pattern classifiers and optimal hyper-plane Optimization problem formulation Statistical properties of optimal hyper-plane. x 2. x. r. w. x 1. ? b/|w|. H. Linear Hyper-plane Classifier.

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Lecture 17 SVM (I): Basic Formulation

Lecture 17 SVM (I): Basic Formulation. Outline. Linear pattern classifiers and optimal hyper-plane Optimization problem formulation Statistical properties of optimal hyper-plane. x 2. x. r. w. x 1. ? b/|w|. H. Linear Hyper-plane Classifier.

247 views • 12 slides

A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos

The paper presents a novel algorithm for object classification in videos based on improved support vector machine (SVM) and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA-SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA-SVM model. The classification rate of 99.39% is achieved with the genetically trained SVM algorithm while 99.1% classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network (ANN), Genetic Artificial neural network (GANN), K-nearest neighbor (K-NN) and K-Means classifiers. Akintola Kolawole G."A Novel GA-SVM Model For Vehicles And Pedestrial Classification In Videos" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: http://www.ijtsrd.com/papers/ijtsrd109.pdf http://www.ijtsrd.com/computer-science/artificial-intelligence/109/a-novel-ga-svm-model-for-vehicles-and-pedestrial-classification-in-videos/akintola-kolawole-g

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