supervised training algorithm

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• Supervised learning — is a machine learning technique for learning a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs. The output of the functioncan be a continuous value (called regression), or… …   Wikipedia

• Cascade correlation algorithm — Cascade Correlation is an architecture and supervised learning algorithm for artificial neural networks developed by Scott Fahlman.Instead of just adjusting the weights in a network of fixed topology, Cascade Correlation begins with aminimal… …   Wikipedia

• Co-training — is a machine learning algorithm used when there are only small amounts of labeled data and large amounts of unlabeled data. One of its uses is in text mining for search engines. It was introduced by Avrim Blum and Tom Mitchell in 1998. Contents 1 …   Wikipedia

• k-nearest neighbor algorithm — KNN redirects here. For other uses, see KNN (disambiguation). In pattern recognition, the k nearest neighbor algorithm (k NN) is a method for classifying objects based on closest training examples in the feature space. k NN is a type of instance… …   Wikipedia

• Semi-supervised learning — In computer science, semi supervised learning is a class of machine learning techniques that make use of both labeled and unlabeled data for training typically a small amount of labeled data with a large amount of unlabeled data. Semi supervised… …   Wikipedia

• Automatic summarization — is the creation of a shortened version of a text by a computer program. The product of this procedure still contains the most important points of the original text. The phenomenon of information overload has meant that access to coherent and… …   Wikipedia

• Support vector machine — Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. Viewing input data as two sets of vectors in an n dimensional space, an SVM will construct a separating hyperplane in that… …   Wikipedia

• Word sense disambiguation — In computational linguistics, word sense disambiguation (WSD) is the process of identifying which sense of a word is used in any given sentence, when the word has a number of distinct senses. For example, consider two examples of the distinct… …   Wikipedia

• Linear classifier — In the field of machine learning, the goal of classification is to group items that have similar feature values, into groups. A linear classifier achieves this by making a classification decision based on the value of the linear combination of… …   Wikipedia

• Clasificador lineal — En el campo del aprendizaje automático, el objetivo del aprendizaje supervisado es usar las características de un objeto para identificar a qué clase (o grupo) pertenece. Un clasificador lineal logra esto tomando una decisión de clasificación… …   Wikipedia Español

• List of important publications in computer science — This is a list of important publications in computer science, organized by field. Some reasons why a particular publication might be regarded as important: Topic creator – A publication that created a new topic Breakthrough – A publication that… …   Wikipedia