Part III Algorithms
Oracle Machine Learning for SQL supports the algorithms listed in Part III. Part III provides basic conceptual information about the algorithms. There is at least one algorithm for each of the machine learning techniques.
Part III contains these chapters:
- Apriori
Learn how to calculate association rules using the Apriori algorithm. - CUR Matrix Decomposition
Learn to use the CUR Matrix Decomposition algorithm for identifying important attributes. - Decision Tree
Oracle Machine Learning supports Decision Tree as one of the classification algorithms. - Expectation Maximization
Learn how to use expectation maximization clustering algorithm. - Explicit Semantic Analysis
Learn how to use Explicit Semantic Analysis (ESA) as an unsupervised algorithm for feature extraction function and as a supervised algorithm for classification. - Exponential Smoothing
Learn about the Exponential Smoothing algorithm. - Generalized Linear Model
Learn how to use Generalized Linear Model (GLM) statistical technique for linear modeling. - k-Means
Oracle Machine Learning supports enhanced k-Means clustering algorithm. Learn how to use the algorithm. - Minimum Description Length
Learn how to use Minimum Description Length, the supervised technique for calculating attribute importance. - Multivariate State Estimation Technique - Sequential Probability Ratio Test
The Multivariate State Estimation Technique - Sequential Probability Ratio Test (MSET-SPRT) algorithm monitors critical processes and detects subtle anomalies. - Naive Bayes
Learn how to use the Naive Bayes classification algorithm. - Neural Network
Learn about the Neural Network algorithms for regression and classification machine learning techniques. - Non-Negative Matrix Factorization
Learn how to use Non-Negative Matrix Factorization (NMF), an unsupervised algorithm, for feature extraction. - O-Cluster
Learn how to use orthogonal partitioning clustering (O-Cluster), an Oracle-proprietary clustering algorithm. - R Extensibility
Learn how to build an analytics model and score in R. The R extensible algorithms are enhanced to support and register additional algorithms for users who use SQL and graphical user interface. - Random Forest
Learn how to use Random Forest as a classification algorithm. - Singular Value Decomposition
Learn how to use Singular Value Decomposition, an unsupervised algorithm for feature extraction. - Support Vector Machine
Learn how to use Support Vector Machine (SVM), a powerful algorithm based on statistical learning theory. - XGBoost
XGBoost is highly-efficient, scalable machine learning algorithm for regression and classification that makes available the XGBoost Gradient Boosting open source package.
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