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Machine learning
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Machine learning and
data mining
Problems
Classification
Clustering
Regression
Anomaly detection
Association rules
Reinforcement learning
Structured prediction
Feature learning
Online learning
Semi-supervised learning
Grammar induction
Supervised learning
(classification • regression)
Decision trees
Ensembles (Bagging, Boosting, Random forest)
k-NN
Linear regression
Naive Bayes
Neural networks
Logistic regression
Perceptron
Support vector machine (SVM)
Relevance vector machine (RVM)
Clustering
BIRCH
Hierarchical
k-means
Expectation-maximization (EM)
DBSCAN
OPTICS
Mean-shift
Dimensionality reduction
Factor analysis
CCA
ICA
LDA
NMF
PCA
t-SNE
Structured prediction
Graphical models (Bayes net, CRF, HMM)
Anomaly detection
k-NN
Local outlier factor
Neural nets
Autoencoder
Deep learning
Multilayer perceptron
RNN
Restricted Boltzmann machine
SOM
Convolutional neural network
Theory
Bias-variance dilemma
Computational learning theory
Empirical risk minimization
PAC learning
Statistical learning
VC theory
Machine learning portal
Computer science portal
Statistics portal
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Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions,:2 rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets ofthe same field.":viiWhen employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.
^ Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning 30: 271–274.
^ Cite error: The named reference bishop was invoked but never defined (see the help page).
^ http://www.britannica.com/EBchecked/topic/1116194/machine-learning
^ Wernick, Yang, Brankov, Yourganov and Strother, Machine Learning in Medical Imaging, IEEE Signal Processing Magazine, vol. 27, no. 4, July 2010, pp. 25-38
^ Mannila, Heikki (1996). Data mining: machine learning, statistics, and databases. Int'l Conf. Scientific and Statistical Database Management. IEEE Computer Society.
^ Friedman, Jerome H. (1998). "Data Mining and Statistics: What's the connection?". Computing Science and Statistics 29 (1): 3–9.
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