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Gauss Elimination and Gauss-Jordan EliminationMath/Linear algebra 2019. 10. 7. 18:23
1. Overview Row reduction is the process of performing row operations to transform any matrix into (reduced) row echelon form. In reduced row echelon form, each successive row of the matrix has less dependencies than the previous, so solving systems of equations is a much easier task. The idea behind row reduction is to convert the matrix into an "equivalent" version in order to simplify certain..
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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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Analysis of variance(ANOVA)Stats/analysisAnalytics 2019. 10. 5. 22:43
1. Overview Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among group means in a sample. The ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sou..
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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..