This post is a follow up on my previous post “R: Text classification using SMOTE and SVM”. I have since gained more experience in R and improved my code. 380 more words

## Tags » SVM

#### 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. 790 more words

#### 【project】Gender Detection

”’

This project is from an interview task. It is accomplished in 3 days, which include literature review, corpus prep and model training. Thus many things are not tuning in to the best way. 1,144 more words

#### 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. 750 more words

#### SVM to Recognize Hand Written Digits in R

Background: https://charleshsliao.wordpress.com/2017/02/24/svm-tuning-based-on-mnist/

```
</pre>
load_image_file <- function(filename) {
ret = list()
f = file(filename,'rb')
readBin(f,'integer',n=1,size=4,endian='big')
ret$n = readBin(f,'integer',n=1,size=4,endian='big')
nrow = readBin(f,'integer',n=1,size=4,endian='big')
ncol = readBin(f,'integer',n=1,size=4,endian='big')
x = readBin(f,'integer',n=ret$n*nrow*ncol,size=1,signed=F)
ret$x = matrix(x, ncol=nrow*ncol, byrow=T)
close(f)
ret
}
load_label_file <- function(filename) {
f = file(filename,'rb')
readBin(f,'integer',n=1,size=4,endian='big')
n = readBin(f,'integer',n=1,size=4,endian='big')
y = readBin(f,'integer',n=n,size=1,signed=F)
close(f)
y
}
#show handwritten digit with show_digit(matriximage$x),n is any number below 60000. 332 more words
```

#### The Mathematics of Machine Learning

@tachyeonz : In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. 6 more words

#### 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. 408 more words