#kernel methods

[mementocurios]

Don’t bend. Don’t water it down. Don’t try to make it logical

[speedysuitfun-blog]

TensorFlow Linear Model Using Kernel Methods - DataFlair

TensorFlow Linear Model, Kernels Methods & Classifier, Preparing MNIST Dataset,logistic regression,Kernel Standard Deviation,regression formula TensorFlow

We will get to know, how to improve the linear model which will use in TensorFlow by adding explicit kernel methods to the model. This article is for the ones who have the knowledge of kernel and Support Vector Machines(SVMs).

[davidrussellschilling]

Kriging& Machine Learning

Image Courtesy: link: http://idibon.com/the-fourth-generation-of-machine-learning-adaptive-learning/ author: idibon.com description: Four generations of machine C O N T E N T S: KEY TOPICS Clustering is a particular type of machine learning unsupervised machine learning, to be precise, meaning that the algorithms must learn from unlabeled data, and as such, they must use inferential methods to…

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[kcf8sksb-blog]

Neural Information Processing. Models and Applications: 17th International Conference, ICONIP 2010, Sydney, Australia, November 21-25, 2010, ... Computer Science and General Issues)

The two volume set LNCS 6443 and LNCS 6444 constitutes the proceedings of the 17th International Conference on Neural Information Processing, ICONIP 2010, held in Sydney, Australia, in November 2010. The 146 regular session papers presented were carefully reviewed and selected from 470 submissions. The papers of part I are organized in topical sections on neurodynamics, computational neuroscience and cognitive science, data and text processing, adaptive algorithms, bio-inspired algorithms, and hierarchical methods. The second volume is structured in topical sections on brain computer interface, Neural Information Processing. Models and Applications: 17th International Conference, ICONIP 2010, Sydney, Australia, November 21-25, 2010, ... Computer Science and General Issues) kernel methods, computational advance in bioinformatics, self-organizing maps and their applications, machine learning applications to image analysis, and applications.

[subtlearray]

Support Vector Machines: A Step-by-Step Introduction

What was the motivation for this tutorial?

This tutorial is my attempt to present the SVMs in a way so that anyone with a passing knowledge of computer science can understand and use it.

What is a support vector machine?

A support vector machine is a supervised machine learning algorithm used for data classification and estimating the relationships between variables (regression analysis). It’s a “supervised” algorithm because there’s an initial training phase involved where you feed the algorithm data that has already been classified (labeled). After this initial training phase is completed, future data sets given to the algorithm can be classified with no or minimal human intervention.

What are the advantages of using a support vector machine?

Many learning algorithms can only do linear classification, using a straight line to separate the data points. But there are algorithms, support vector machines being one of them, that can also do non-linear classification using a kernel method.

A kernel method, in short, is an equation that can pull data points apart into 3-dimensional space, and, instead of using a line as a separator, it uses something called a hyperplane, that, from a vertical standpoint, can take nonlinear forms. Nonlinear classification provides a more sophisticated way to classify complex data sets that can't easily be separated by a straight line.

What’s the fastest way to get started with support vector machines?

There are several software packages and libraries available that you can use. Two of the most popular are Rapid Miner and libsvm.

How do I use libsvm?

• For Windows: Download and extract the libsvm zip file, and move the windows files into the folder where your data files are located. • For Ubuntu Linux: Enter sudo apt-get install libsvm-tools into the Terminal. • In the command line (cmd in Winndows, Terminal in Linux), run the svm-train executable on your training data (ex. a1a. / rename this file to a1a.train for convenience) to create a model of your data. Use example: svm-train a1a.train • Run the svm-predict executable on test data (ex. ata.t / rename this file to a1a.test for convenience) and on the model file created by svm-train. End the command with the name of the predictions output file you’d like for it to create. Use example: svm-predict a1a.test a1a.train.model a1a.out • The 1 and -1 classification labels in the output file correlate with the order of the data entries in the test file, and were learned from the training file.

Where should I go if I want to learn more?

• Knowledge Discovery with Support Vector Machines by Lutz H. Hamel

[lumfcva-blog]

Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)

Learning Theory: 20th Annual Conference on Learning Theory, COLT 2007, San Diego, CA, USA, June 13-15, 2007, Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. The 41 revised full papers presented together with 5 articles on open problems and 2 invited lectures were carefully reviewed and selected from a total of 92 submissions. The papers cover a wide range of topics and are organized in topical sections on unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, other approaches, and open problems.