enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Support vector machine - Wikipedia

    en.wikipedia.org/wiki/Support_vector_machine

    The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to the behavior of the hinge loss.

  3. Least-squares support vector machine - Wikipedia

    en.wikipedia.org/wiki/Least-squares_support...

    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.

  4. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often ...

  5. Regularization perspectives on support vector machines

    en.wikipedia.org/wiki/Regularization...

    SVM algorithms categorize binary data, with the goal of fitting the training set data in a way that minimizes the average of the hinge-loss function and L2 norm of the learned weights. This strategy avoids overfitting via Tikhonov regularization and in the L2 norm sense and also corresponds to minimizing the bias and variance of our estimator ...

  6. Cross-validation (statistics) - Wikipedia

    en.wikipedia.org/wiki/Cross-validation_(statistics)

    A single k-fold cross-validation is used with both a validation and test set. The total data set is split into k sets. One by one, a set is selected as test set. Then, one by one, one of the remaining sets is used as a validation set and the other k - 2 sets are used as training sets until all possible combinations have been evaluated. Similar ...

  7. Ranking SVM - Wikipedia

    en.wikipedia.org/wiki/Ranking_SVM

    In machine learning, a ranking SVM is a variant of the support vector machine algorithm, which is used to solve certain ranking problems (via learning to rank). The ranking SVM algorithm was published by Thorsten Joachims in 2002. [1] The original purpose of the algorithm was to improve the performance of an internet search engine.

  8. 26 Thanksgiving side dishes ranked from healthiest to ...

    www.aol.com/lifestyle/26-thanksgiving-side...

    10 healthiest Thanksgiving side dishes. 1. Roasted Brussels sprouts. 2. Butternut squash soup. 3. Awesome sausage, apple, and cranberry stuffing. 4. Simple roasted butternut squash

  9. Elastic net regularization - Wikipedia

    en.wikipedia.org/wiki/Elastic_net_regularization

    The authors showed that for every instance of the elastic net, an artificial binary classification problem can be constructed such that the hyper-plane solution of a linear support vector machine (SVM) is identical to the solution (after re-scaling). The reduction immediately enables the use of highly optimized SVM solvers for elastic net problems.