Handwritten Digit Classification and Sample Complexity using Kernel Perceptron

Computer science

Contents
Introduction...........................................................................................................................................1
Supervised Learning..............................................................................................................................1
Kernel Perceptron..................................................................................................................................1
Adding Kernel.......................................................................................................................................1
Training and Testing the Kernel Perceptron..........................................................................................2
Generalizing to K Classes......................................................................................................................2
Cross validation.....................................................................................................................................2
Confusion Matrix..................................................................................................................................3
Gaussian Kernel....................................................................................................................................3
Polynomial Kernel.................................................................................................................................3
Sparse Learning.....................................................................................................................................4
Sample Complexity...............................................................................................................................4
Bias Variance Trade Off........................................................................................................................4
Linear Regression..................................................................................................................................4
1-Nearest Neighbour Algorithm............................................................................................................4
Conclusion.............................................................................................................................................5
References.............................................................................................................................................5

Introduction
In this project to develop the handwritten digit classification and sample complexity. We
are using the python code to develop the handwritten digit classification. The input sources
such as paper document, photographs, touch screens and other devices. The text image
converts to letter by using python code. The handwritten digit also called as kernel
perceptron.Perceptron is an algorithm and it is one of the supervised learning. It has two type
of classifier. One is linear classifier and another one is binary classifier. The binary classifier
using to classify the binary in the set. It represent by vector of number. The second task to
satisfy the sample complexity. It based on linear regression and winnow.
Supervised Learning
The supervised learning is the machine learning and it based on output and input. We
import the training dataset and testing the dataset. In this algorithm analyze the data and
produce the inferred function (CAI, 2010). The training dataset needs to be representative of
real world use of the function. We import the data by using python code and it makes map or
graph related to graph. In this project we implement the kernel perceptorn using python. We
are using the supervised learning algorithm for implement the handwritten digit classification
and sample complexity.
Kernel Perceptron
The task is quasi realistic and working with the large dataset. The text image converts
into latter using the python. First step to import the training dataset. The perceptron
generalize in two ways. The first way to use the kernel function for generalize the
perceptron.The second way generalize the perceptron into a majority network perceptron.It
has two classes and it separate k classes.
Adding Kernel
We adding kernel one by one and it allows to map. It is linear function and it performs
the ploynominal.The kernel mainly using the operating system. It is center part of operating
system.It manages the operating system and hardware also. The micro kernel and monolithic
kernel is kernel types.