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Pattern Recognition
PATTERN RECOGNITION is a branch of machine learning that focuses on the recognition of patterns and regularities in data , although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition
Pattern recognition
systems are in many cases trained from labeled "training" data (supervised learning ), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning ). The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods and originates from artificial intelligence , whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use
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Birch
A BIRCH is a thin-leaved deciduous hardwood tree of the genus BETULA (/ˈbɛtjʊlə/ ), in the family Betulaceae
Betulaceae
, which also includes alders , hazels , and hornbeams . It is closely related to the beech -oak family Fagaceae
Fagaceae
. The genus Betula contains 30 to 60 known taxa of which 11 are on the IUCN 2011 Green List of Threatened Species. They are a typically rather short-lived pioneer species widespread in the Northern Hemisphere, particularly in northern temperate and boreal climates
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Hierarchical Clustering
In data mining and statistics , HIERARCHICAL CLUSTERING (also called HIERARCHICAL CLUSTER ANALYSIS or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types: * AGGLOMERATIVE: This is a "bottom up" approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. * DIVISIVE: This is a "top down" approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram
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K-means Clustering
K-MEANS CLUSTERING is a method of vector quantization , originally from signal processing , that is popular for cluster analysis in data mining . k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean , serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells . The problem is computationally difficult ( NP-hard
NP-hard
); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum . These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms
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Expectation-maximization Algorithm
In statistics , an EXPECTATION–MAXIMIZATION (EM) ALGORITHM is an iterative method to find maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models , where the model depends on unobserved latent variables . The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. EM clustering of Old Faithful eruption data. The random initial model (which, due to the different scales of the axes, appears to be two very flat and wide spheres) is fit to the observed data. In the first iterations, the model changes substantially, but then converges to the two modes of the geyser
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Support Vector Machine
In machine learning , SUPPORT VECTOR MACHINES (SVMS, also SUPPORT VECTOR NETWORKS ) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis . Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall
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Relevance Vector Machine
In mathematics , a RELEVANCE VECTOR MACHINE (RVM) is a machine learning technique that uses Bayesian inference
Bayesian inference
to obtain parsimonious solutions for regression and probabilistic classification . The RVM has an identical functional form to the support vector machine , but provides probabilistic classification
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Random Forest
RANDOM FORESTS or random decision forests are an ensemble learning method for classification , regression and other tasks, that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Random decision forests correct for decision trees' habit of overfitting to their training set. :587–588 The first algorithm for random decision forests was created by Tin Kam Ho using the random subspace method , which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg. An extension of the algorithm was developed by Leo Breiman and Adele Cutler, and "Random Forests" is their trademark
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Naive Bayes Classifier
In machine learning , NAIVE BAYES CLASSIFIERS are a family of simple probabilistic classifiers based on applying Bayes\' theorem with strong (naive) independence assumptions between the features. Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s, :488 and remains a popular (baseline) method for text categorization , the problem of judging documents as belonging to one category or the other (such as spam or legitimate , sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines . It also finds application in automatic medical diagnosis . Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem
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Logistic Regression
In statistics , LOGISTIC REGRESSION, or LOGIT REGRESSION, or LOGIT MODEL is a regression model where the dependent variable (DV) is categorical . This article covers the case of a binary dependent variable —that is, where it can take only two values, "0" and "1", which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick. Cases where the dependent variable has more than two outcome categories may be analysed in multinomial logistic regression , or, if the multiple categories are ordered , in ordinal logistic regression . In the terminology of economics , logistic regression is an example of a qualitative response/discrete choice model . Logistic regression
Logistic regression
was developed by statistician David Cox in 1958. The binary logistic model is used to estimate the probability of a binary response based on one or more predictor (or independent) variables (features)
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Perceptron
In machine learning , the PERCEPTRON is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not). It is a type of linear classifier , i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector . The algorithm allows for online learning , in that it processes elements in the training set one at a time. The perceptron algorithm dates back to the late 1950s. Its first implementation, in custom hardware, was one of the first artificial neural networks to be produced
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DBSCAN
DENSITY-BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel , Jörg Sander and Xiaowei Xu in 1996. It is a density-based clustering algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors ), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature. In 2014, the algorithm was awarded the test of time award (an award given to algorithms which have received substantial attention in theory and practice) at the leading data mining conference, KDD
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OPTICS Algorithm
ORDERING POINTS TO IDENTIFY THE CLUSTERING STRUCTURE (OPTICS) is an algorithm for finding density-based clusters in spatial data. It was presented by Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel and Jörg Sander. Its basic idea is similar to DBSCAN
DBSCAN
, but it addresses one of DBSCAN's major weaknesses: the problem of detecting meaningful clusters in data of varying density. In order to do so, the points of the database are (linearly) ordered such that points which are spatially closest become neighbors in the ordering. Additionally, a special distance is stored for each point that represents the density that needs to be accepted for a cluster in order to have both points belong to the same cluster. This is represented as a dendrogram
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Non-negative Matrix Factorization
NON-NEGATIVE MATRIX FACTORIZATION (NMF or NNMF), also NON-NEGATIVE MATRIX APPROXIMATION is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered. Since the problem is not exactly solvable in general, it is commonly approximated numerically. NMF finds applications in such fields as computer vision , document clustering , chemometrics , audio signal processing and recommender systems
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