machine-learning algorithm
1Machine learning — is a subfield of artificial intelligence that is concerned with the design and development of algorithms and techniques that allow computers to learn . In general, there are two types of learning: inductive, and deductive. Inductive machine… …
2Machine Learning — Maschinelles Lernen ist ein Oberbegriff für die „künstliche“ Generierung von Wissen aus Erfahrung: Ein künstliches System lernt aus Beispielen und kann nach Beendigung der Lernphase verallgemeinern. Das heißt, es lernt nicht einfach die Beispiele …
3Online machine learning — In machine learning, online learning is a model of induction that learns one instance at a time. The goal in online learning is to predict labels for instances. For example, the instances could describe the current conditions of the stock market …
4Weka (machine learning) — Infobox Software name = Weka caption = Weka 3.5.5 with Explorer window open with Iris UCI dataset developer = University of Waikato latest release version = 3.4.13 (book), 3.5.8 (developer) latest release date = July 16, 2008 operating system =… …
5Classification in machine learning — See also: Pattern recognition This section needs integrating with Statistical classification (Discuss). Integration means cross linking and distinguishing (to/from each other), or sometimes merging (if consensus suggests). In machine learning and …
6Monte Carlo Machine Learning Library (MCMLL) — The Monte Carlo Machine Learning Library (MCMLL) is an open source C++ template library which already relies on some C++0x specs. MCMLL is licensed under the GNU GPL. It is developed under the 64 bit Linux OS. MCMLL should be usable on other… …
7Java Machine Learning Library — The Java Machine Learning Library is a set of reference implementations of machine learning algorithms. These algorithms are well documented, both in the source code as on the documentation site. Besides real machine learning algorithms also a… …
8Overfitting (machine learning) — For the statistical concept see OverfittingThe concept of overfitting is important in machine learning. Usually a learning algorithm is trained using some set of training examples, i.e. exemplary situations for which the desired output is known.… …
9Bootstrapping (machine learning) — In machine learning, bootstrapping is a general technique that iteratively trains and evaluates a classifier in order to improve its performance. In object detection, it is often used in conjunction with AdaBoost algorithm for efficient… …
10Transduction (machine learning) — In logic, statistical inference, and supervised learning,transduction or transductive inference is reasoning fromobserved, specific (training) cases to specific (test) cases. In contrast, induction is reasoning from observed training casesto… …
