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Files are tracked using the same history format as in RCS, with a hidden directory containing a corresponding history file for each file in the repository. CVS uses delta compression for efficient storage of different versions of the same file. This works well with large text files with few changes from one version to the next.
If is a normed space (as is the case for SVM), a particularly effective technique is to consider only those hypotheses for which ‖ ‖ <. This is equivalent to imposing a regularization penalty R ( f ) = λ k ‖ f ‖ H {\displaystyle {\mathcal {R}}(f)=\lambda _{k}\lVert f\rVert _{\mathcal {H}}} , and solving the new optimization problem
The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels.
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1]
update: Update the files in a working copy with the latest version from a repository; lock: Lock files in a repository from being changed by other users; add: Mark specified files to be added to repository at next commit; remove: Mark specified files to be removed at next commit (note: keeps cohesive revision history of before and at the remove.)
Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.
Schölkopf developed SVM methods achieving world record performance on the MNIST pattern recognition benchmark at the time. [2] With the introduction of kernel PCA, Schölkopf and coauthors argued that SVMs are a special case of a much larger class of methods, and all algorithms that can be expressed in terms of dot products can be generalized to a nonlinear setting by means of what is known ...
The original form of the pattern, appearing in Pattern Languages of Program Design 3, [2] has data races, depending on the memory model in use, and it is hard to get right. Some consider it to be an anti-pattern. [3] There are valid forms of the pattern, including the use of the volatile keyword in Java and explicit memory barriers in C++. [4]