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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.
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.
The training and test-set errors can be measured without bias and in a fair way using accuracy, precision, Auc-Roc, precision-recall, and other metrics. Regularization perspectives on support-vector machines interpret SVM as a special case of Tikhonov regularization, specifically Tikhonov regularization with the hinge loss for a loss function.
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 ...
His belief that scientific progress came from conceptual breakthroughs fueled his formulation and reformulation of a theory of motivation. He was one of the first in psychology to incorporate rigorous mathematical models in his theories and to use computer simulations of these models for experimentation.
Intrinsic motivation in the study of artificial intelligence and any robotics is a mechanism for enabling artificial agents (including robots) to exhibit inherently rewarding behaviours such as exploration and curiosity, grouped under the same term in the study of psychology. Psychologists consider intrinsic motivation in humans to be the drive ...
The traditional discipline studying motivation is psychology. It investigates how motivation arises, which factors influence it, and what effects it has. [8] Motivation science is a more recent field of inquiry focused on an integrative approach that tries to link insights from different subdisciplines. [9]
The 3H-model of motivation ("3H" stands for the "three components of motivation") was developed by Hugo M. Kehr of UC Berkeley. The 3C-model is an integrative, empirically validated theory of motivation that can be used for systematic motivation diagnosis and intervention.