support vector machine

More discussion of this can be found in the support vector machine article.

For example, the engineer may choose to use support vector machines or decision trees.

It is the most popular kernel function used in support vector machine classification.

See the article on Support Vector Machines for more details.

It is the theoretical framework underlying support vector machines.

Structural risk minimization at the support vector machines website.

Cortes' research covers a wide range of topics in machine learning, including support vector machines and data mining.

A typical data mining based prediction uses support vector machines, decision trees, or neural networks.

For example, if , a base learner could be a linear soft margin support vector machine.

See support vector machines and maximum-margin hyperplane for details.

The following learning method can be any of the already mentioned machine learning methods, e.g. support vector machines.

This is the bias used in Support Vector Machines.

Support vector machines provably maximize the margin of the separating hyperplane.

Support vector machines have proven particularly useful for predicting the locations of turns, which are difficult to identify with statistical methods.

In particular, support vector machines find a hyperplane that separates the feature space into two classes with the maximum margin.

Furthermore, there is often no need to compute directly during computation, as is the case with support vector machines.

Other linear classification algorithms include Winnow, support vector machine and logistic regression.

In recent research, kernel-based methods such as support vector machines have shown superior performance in supervised learning.

The last part of VC theory introduced a well-known learning algorithm: the support vector machine.

He has made particular contributions with support vector machines and kernel PCA.

Platt invented Sequential minimal optimization, a widely used method for training support vector machines.

Of particular prominence is the generalization error bound on boosting algorithms and support vector machines.

Classification is driven by a support vector machine using a series of time-independent acoustic features that act like a fingerprint.

While at AT&T, Vapnik and his colleagues developed the theory of the support vector machine.

Formally, a transductive support vector machine is defined by the following primal optimization problem: