In Python we can build SVM model for classification with sklearn library. We can use basic linearsvc or svc with more parameters to tune. We use the data from sklearn library, and the IDE is sublime text3. Most of the code comes from the book: https://www.goodreads.com/book/show/32439431-introduction-to-machine-learning-with-python?from_search=true

# Tag: SVM

## Bank Loan Estimation with SVM and Logistic Regression

Use the bank marketing dataset from UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Bank+Marketing). There are no the only best C or Gamma value for SVM since the data and the problem we try to solve are different. From the observation above, a higher gamma value would result a slightly better accuracy. But the cost would not… Continue reading Bank Loan Estimation with SVM and Logistic Regression

## Quick Example of Parallel Computation in R for SVM/Random Forest, with MNIST and Credit Data

It is generally acknowledged that SVM algorithm is relatively slow to train, even with tuning parameters such as cost and kernel. The general way to boost the speed is to apply packages of "parallel" "do parallel" "doSNOW" and for each function. Data and background: Data and background: https://charleshsliao.wordpress.com/2017/02/24/svm-tuning-based-on-mnist/ It is not ensured that we can increase… Continue reading Quick Example of Parallel Computation in R for SVM/Random Forest, with MNIST and Credit Data

## Kernels, SVM and a Letter Recognition Example

This article is still about SVM and related parameters, especially the one called Kernel. We can use different Kernel methods to project or map data into higher dimension space. This would be typically useful for non-linear problems in real life. The linear kernel does not transform the data at all The polynomial kernel of degree… Continue reading Kernels, SVM and a Letter Recognition Example

## SVM to Recognize Hand Written Digits in R

## SVM(e1071 of R) Tuning with MNIST

Background: Handwriting recognition is a well-studied subject in computer vision and has found wide applications in our daily life (such as USPS mail sorting). In this project, we will explore various machine learning techniques for recognizing handwriting digits. The dataset you will be using is the well-known MINST dataset. (1) The MNIST database of handwritten… Continue reading SVM(e1071 of R) Tuning with MNIST