silver machine-- silver ratio-- silver rectangle-- silverman–toeplitz theorem-- silverman's game-- sim pencil game -- simalto-- simerr-- similarity geometry -- similarity invariance-- similarity measure-- similarity system of triangles-- simion stoilow prize-- simon problems-- simon stevin journal -- simons center for geometry and physics .
in machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. boosting is based on the question posed by kearns and valiant 1988, 1989 : 'can a set of weak learners create a single strong learner?' a weak learner is defined to be a .
cascading classifiers are trained with several hundred 'positive' sample views of a particular object and arbitrary 'negative' images of the same size. after the classifier is trained it can be applied to a region of an image and detect the object in question.
in machine learning and statistics, classification is the problem of identifying to which of a set of categories sub-populations a new observation belongs, on the basis of a training set of data containing observations or instances whose category membership is known. examples are assigning a given email to the 'spam' or 'non-spam' class, and assigning a diagnosis to a given patient based .
classifier chains is a machine learning method for problem transformation in multi-label classification.it combines the computational efficiency of the binary relevance method while still being able to take the label dependencies into account for classification.
pattern recognition is the automated recognition of patterns and regularities in data.pattern recognition is closely related to artificial intelligence and machine learning, together with applications such as data mining and knowledge discovery in databases kdd , and is often used interchangeably with these terms. however, these are distinguished: machine learning is one approach to pattern .
a receiver operating characteristic curve, or roc curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. the roc curve is created by plotting the true positive rate tpr against the false positive rate fpr at various threshold settings.
the evaluation of binary classifiers compares two methods of assigning a binary attribute, one of which is usually a standard method and the other is being investigated. there are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals.
in machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles
as classification is a particular case of regression when the response variable is categorical, mlps make good classifier algorithms. mlps were a popular machine learning solution in the 1980s, finding applications in diverse fields such as speech recognition, image recognition, .
statistical binary classification. statistical classification is a problem studied in machine learning. it is a type of supervised learning, a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories. when there are only two categories the problem is known as statistical binary classification.
the name 'extreme learning machine' elm was given to such models by its main inventor guang-bin huang. according to their creators, these models are able to produce good generalization performance and learn thousands of times faster than networks trained using backpropagation.
types of machines. spinning tube, commonly called spiral ct, or helical ct is an imaging technique in which an entire x-ray tube is spun around the central axis of the area being scanned. these are the dominant type of scanners on the market because they have been manufactured longer and offer lower cost of production and purchase. the main .
the matthews correlation coefficient mcc is used in machine learning as a measure of the quality of binary two-class classifications, introduced by biochemist brian w. matthews in 1975. although the mcc is equivalent to karl pearson's phi coefficient, which was developed decades earlier, the term mcc is widely used in the field of bioinformatics.. the coefficient takes into account true .
the algorithm has a loose relationship to the k-nearest neighbor classifier, a popular machine learning technique for classification that is often confused with k-means due to the name. applying the 1-nearest neighbor classifier to the cluster centers obtained by k-means classifies new data into