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  2. 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.

  3. 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.

  4. 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 ...

  5. Polynomial kernel - Wikipedia

    en.wikipedia.org/wiki/Polynomial_kernel

    The hyperplane learned in feature space by an SVM is an ellipse in the input space. In machine learning , the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original ...

  6. Sample complexity - Wikipedia

    en.wikipedia.org/wiki/Sample_complexity

    Now, four coplanar points in a square cannot be shattered by any affine function, since no affine function can be positive on two diagonally opposite vertices and negative on the remaining two. Thus, the VC dimension of H {\displaystyle {\mathcal {H}}} is d + 1 {\displaystyle d+1} , so it is finite.

  7. Sequential minimal optimization - Wikipedia

    en.wikipedia.org/wiki/Sequential_minimal...

    It is known as the "chunking algorithm". The algorithm starts with a random subset of the data, solves this problem, and iteratively adds examples which violate the optimality conditions. One disadvantage of this algorithm is that it is necessary to solve QP-problems scaling with the number of SVs.

  8. To make this 20-minute vegan curry even faster, buy precut veggies from the salad bar at the grocery store. To make it a full, satisfying dinner, serve over cooked brown rice.

  9. Integer-valued function - Wikipedia

    en.wikipedia.org/wiki/Integer-valued_function

    In mathematics, an integer-valued function is a function whose values are integers.In other words, it is a function that assigns an integer to each member of its domain.. The floor and ceiling functions are examples of integer-valued functions of a real variable, but on real numbers and, generally, on (non-disconnected) topological spaces integer-valued functions are not especially useful.