CSC SVMs and other classifiers differ significantly especially

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CSC 4810-Artificial Intelligence
Support Vector Machine
SVM is an implementation of Support Vector Machine (SVM). Support
Vector Machine was developed by Vapnik. The main futures of the program
are the following: for the problem of pattern recognition, for the problem
of regression, for the problem of learning a ranking function. Underlying
the success of SVM are mathematical foundations of statistical learning
theory. Rather than minimizing the training error, SVMs minimize
structural risk which express and upper bound on generalization error.

SVM are popular because they usually achieve good error rates and can
handle unusual types of data like text, graphs, and images.

SVM’s leading idea is to classify the input data separating them
within a decision threshold lying far from the two classes and scoring a
low number of errors. SVM’s are used for pattern recognition. Basically,
a data set is used to “train” a particular machine. This machine can learn
more by retraining it with the old data plus the new data. The trained
machine is as unique as the data that was used to train it and the
algorithm that was used to process the data. Once a machine is trained, it
can be used to predict how closely a new data set matches the trained
machine. In other words, Support Vector Machines are used for pattern
recognition. SVM uses the following equation to trained the Vector
Machine: H(x) = sign {wx + b}
w = weight vector
b = threshold
The generalization abilities of SVMs and other classifiers differ
significantly especially when the number of training data is small. This
means that if some mechanism to maximize margins of decision boundaries is
introduced to non-SVM type classifiers, their performance degradation will
be prevented when the class overlap is scarce or non-existent. In the
original SVM, the n-class classification problem is converted into n two-
class problems, and in the ith two-class problem we determine the optimal
decision function that separates class i from the remaining classes. In
classification, if one of the n decision functions classifies an unknown
datum into a definite class, it is classified into that class. In this
formulation, if more than one decision function classifies a datum into
definite classes, or no decision functions classify the datum into a
definite class, the datum is unclassifiable.

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To resolve unclassifiable regions for SVMswe discuss four types of
SVMs: one against all SVMs; pairwise SVMs; ECOC (Error Correction Output
Code) SVMs; all at once SVMs; and their variants. Another problem of SVM
is slow training. Since SVM are trained by a solving quadratic programming
problem with number of variables equals to the number of training data,
training is slow for a large number of training data. We discuss training
of Sims by decomposition techniques combined with a steepest ascent method.

Support Vector Machine algorithm also plays big role in internet
industry. For example, the Internet is huge, made of billions of documents
that are growing exponentially every year. However, a problem exists in
trying to find a piece of information amongst the billions of growing
documents. Current search engines scan for key words in the document
provided by the user in a search query. Some search engines such as Google
even go as far as to offer page rankings by users who have previously
visited the page. This relies on other people ranking the page according
to their needs. Even though these techniques help millions of users a day
retrieve their information, it is not even close to being an exact science.

The problem lies in finding web pages based on your search query that
actually contain the information you are looking for.

Here is the figure of SVM algorithm:
It is important to understand the mechanism behind the SVM. The SVM
implement the Bayes rule in interesting way. Instead of estimating P(x) it
estimates sign P(x)-1/2. This is advantage when our goal is binary
classification with minimal excepted misclassification rate. However, this
also means that in some other situation the SVM needs to be modified and
should not be used as is.

In conclusion, Support Vector Machine support lots of real world
applications such as text categorization, hand-written character
recognition, image classification, bioinformatics, etc. Their first
introduction in early 1990s lead to a recent explosion of applications and
deepening theoretical analysis that was now established Support Vector
Machines along with neural networks as one of standard tools for machine
learning and data mining. There is a big use of Support Vector Machine in
Medical Field.

Boser, B., Guyon, I and Vapnik, V.N.(1992). A training algorithm for
optimal margin classifiers.

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