Explanation
SVMs can be used to solve various real world problems:
* SVMs are helpful in text and hypertext categorization as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
* Classification of images can also be performed using SVMs. Experimental results show that SVMs achieve significantly higher search accuracy than traditional query refinement schemes after just three to four rounds of relevance feedback.
* SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly.
* Hand-written characters can be recognized using SVM

Explanation
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so P(E1|E2) = P(E1.E2)/P(E2) P(E1,E2,E3)/P(E2,E3) If the events are A and B respectively, this is said to be "the probability of A given B" It is commonly denoted by P(A|B): or sometimes PB(A). In case that both "A" and "B" are categorical variables, conditional probability table is typically used to represent the conditional probability.

Explanation
Feature Selection:
The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate analysis evaluates. Them according to the magnitude of the components of the discriminate vector.

Explanation
A linear scaling of the input variables (that may be caused by a change of units for the measurements) is sufficient to modify the PCA results. Feature selection methods that are sufficient for simple distributions of the patterns belonging to different classes can fail in classification tasks with complex decision boundaries. In addition, methods based on a linear dependence (like the correlation) cannot take care of arbitrary relations between the pattern coordinates and the different classes. On the contrary, the mutual information can measure arbitrary relations between variables and it does not depend on transformations acting on the different variables.
This item concerns itself with feature selection for a text classification problem and references mutual information criteria. Mutual information is a bit more sophisticated than just selecting based on the simple correlation of two numbers because it can detect non-linear relationships that will not be identified by the correlation. Whenever possible: mutual information is a better feature selection technique than correlation.
Mutual information is a quantification of the dependency between random variables. It is sometimes contrasted with linear correlation since mutual information captures nonlinear dependence.
Correlation analysis provides a quantitative means of measuring the strength of a linear relationship between two vectors of data. Mutual information is essentially the measure of how much "knowledge" one can gain of a certain variable by knowing the value of another variable.