MLAI
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Clustering analysisMLAI/Clustering 2019. 10. 6. 18:23
1. Overview Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is the main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern re..
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Naive Bayes classifierMLAI/Classification 2019. 10. 6. 18:09
1. Overview 2. Bayes Theorem 2.1 Formula $$P(A|B)=\frac{P(B|A)\times P(A)}{P(B)}$$ 2.2 Example 3. Classifier 3.1 Procedure 3.1.1 Step 1 3.1.2 Step 2 3.1.3 Step 3 The first time we're going to apply it to find out what is the probability that this person walks given his features and X over here is the features or presents the features of that data point. And then we're going to look at all the po..
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Feature selectionMLAI/DimensionalityReduction 2019. 10. 6. 14:59
1. Overview In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/us..
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K-means clusteringMLAI/Clustering 2019. 10. 5. 23:59
1. Overview 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 ce..
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Fisher's linear discriminant(Linear discriminant analysis, LDA)MLAI/DimensionalityReduction 2019. 10. 5. 22:25
1. Overview Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistic, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a..
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Principal component analysis(PCA)MLAI/DimensionalityReduction 2019. 10. 5. 17:32
1. Overview Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. This transformation is defined in such a way that the first principal compo..
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Artificial neural network(ANN)MLAI/DeepLearning 2019. 10. 5. 13:39
1. Overview Artificial neural networks (ANN) or connectionist systems are computing systems that are inspired by, but not identical to, biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules. For example, in image recognition, they might learn to identify images that cont..
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Hidden Markov Model(HMM)MLAI/Classification 2019. 10. 4. 21:47
1. Overview HMM is called hidden because only the symbols emitted by the system are observable, not the underlying random walk between states. An HMM can be visualized as a finite state machine. it generates a protein sequence by emitting amino acids as it progresses through a series of states. 2. Description 2.1 Definition of Markov Model There are four common Markov models used in different si..