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EW 104 : EW against a new generation of threats / EW against a new generation of threats by Adamy, David

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Mastering Java Machine Learning. by Kamath, Dr. Uday

Table of Contents: “…Case Study -- Horse Colic Classification -- Business problem -- Machine learning mapping -- Data analysis -- Label analysis -- Features analysis -- Supervised learning experiments -- Weka experiments -- RapidMiner experiments -- Results, observations, and analysis -- Summary -- References -- Chapter 3: Unsupervised Machine Learning Techniques -- Issues in common with supervised learning -- Issues specific to unsupervised learning -- Feature analysis and dimensionality reduction -- Notation -- Linear methods -- Principal component analysis (PCA) -- Random projections (RP) -- Multidimensional Scaling (MDS) -- Nonlinear methods -- Kernel Principal Component Analysis (KPCA) -- Manifold learning -- Clustering -- Clustering algorithms -- k-Means -- DBSCAN -- Mean shift -- Expectation maximization (EM) or Gaussian mixture modeling (GMM) -- Hierarchical clustering -- Self-organizing maps (SOM) -- Spectral clustering -- Affinity propagation -- Clustering validation and evaluation -- Internal evaluation measures -- External evaluation measures -- Outlier or anomaly detection -- Outlier algorithms -- Statistical-based -- Distance-based methods -- Density-based methods -- Clustering-based methods -- High-dimensional-based methods -- One-class SVM -- Outlier evaluation techniques -- Supervised evaluation -- Unsupervised evaluation -- Real-world case study -- Tools and software -- Business problem -- Machine learning mapping -- Data collection -- Data quality analysis -- Data sampling and transformation -- Feature analysis and dimensionality reduction -- Observations on feature analysis and dimensionality reduction -- Clustering models, results, and evaluation -- Observations and clustering analysis -- Outlier models, results, and evaluation -- Summary -- References -- Chapter 4: Semi-Supervised and Active Learning -- Semi-supervised learning.…”
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