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.