cv.SVM - mexopencv

Support Vector Machines

Originally, support vector machines (SVM) was a technique for building an optimal binary (2-class) classifier. Later the technique was extended to regression and clustering problems. SVM is a partial case of kernel-based methods. It maps feature vectors into a higher-dimensional space using a kernel function and builds an optimal linear discriminating function in this space or an optimal hyper-plane that fits into the training data. In case of SVM, the kernel is not defined explicitly. Instead, a distance between any 2 points in the hyper-space needs to be defined.

The solution is optimal, which means that the margin between the separating hyper-plane and the nearest feature vectors from both classes (in case of 2-class classifier) is maximal. The feature vectors that are the closest to the hyper-plane are called support vectors, which means that the position of other vectors does not affect the hyper-plane (the decision function).

SVM implementation in OpenCV is based on [LibSVM].

cv.SVM implements the "one-against-one" approach for multi-class classification. If N is the number of classes, then N*(N-1)/2 classifiers are constructed, each one trained with data from two classes for every pair of distinct classes (N choose 2).

References

[LibSVM]:

C.-C. Chang and C.-J. Lin. "LIBSVM: a library for support vector machines", ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27, 2011. PDF

[Burges98]:

C. Burges. "A tutorial on support vector machines for pattern recognition", Knowledge Discovery and Data Mining 2(2), 1998 CiteSeerX

Superclasses	handle
Sealed	false
Construct on load	false

C	Parameter `C` of a SVM optimization problem.
ClassWeights	Optional weights in the `C_SVC` problem, assigned to particular
Coef0	Parameter `coef0` of a kernel function.
Degree	Parameter `degree` of a kernel function.
Gamma	Parameter `gamma` of a kernel function.
KernelType	Type of a SVM kernel.
Nu	Parameter `nu` of a SVM optimization problem.
P	Parameter `epsilon` of a SVM optimization problem.
TermCriteria	Termination criteria.
Type	of a SVM formulation.
id	Object ID

	addlistener	Add listener for event.
	calcError	Computes error on the training or test dataset
	clear	Clears the algorithm state
	delete	Destructor
	empty	Returns true if the algorithm is empty
	eq	== (EQ) Test handle equality.
	findobj	Find objects matching specified conditions.
	findprop	Find property of MATLAB handle object.
	ge	>= (GE) Greater than or equal relation for handles.
	getDecisionFunction	Retrieves the decision function
	getDefaultName	Returns the algorithm string identifier
	getSupportVectors	Retrieves all the support vectors
	getUncompressedSupportVectors	Retrieves all the uncompressed support vectors of a linear SVM
	getVarCount	Returns the number of variables in training samples
	gt	> (GT) Greater than relation for handles.
	isClassifier	Returns true if the model is a classifier
	isTrained	Returns true if the model is trained
Sealed	isvalid	Test handle validity.
	le	<= (LE) Less than or equal relation for handles.
	listener	Add listener for event without binding the listener to the source object.
	load	Loads algorithm from a file or a string
	lt	< (LT) Less than relation for handles.
	ne	~= (NE) Not equal relation for handles.
	notify	Notify listeners of event.
	predict	Predicts response(s) for the provided sample(s)
	save	Saves the algorithm parameters to a file or a string
	setCustomKernel	Initialize with custom kernel
	train	Trains the statistical model
	trainAuto	Trains an SVM with optimal parameters