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The Use Cases of One-Class SVM in Oracle

BTS Sales Engagement Hub, Oracle Artificial Intelligence (AI)

Not all machine learning problems are about predicting a known outcome. Sometimes the more interesting question is whether something looks unusual when compared with everything else. This is the idea behind One Class Support Vector Machine (One Class SVM). Supported...

Uses of Non-Negative Matrix Factorisation in Oracle ML

BTS Sales Engagement Hub, Oracle Artificial Intelligence (AI)

Large datasets can contain a huge amount of information, this does not always make analysis easier. In many cases, the challenge is finding a simpler way to represent the data while keeping the patterns that actually matter. Non Negative Matrix Factorisation (NMF) is...

How Does the Supported Oracle Algorithm O-Cluster Function

BTS Sales Engagement Hub, Oracle Artificial Intelligence (AI)

Finding significant data groups within datasets can be a challenge, especially when there are no predefined categories to work with. O Cluster, short for Orthogonal Partitioning Clustering, is an unsupervised machine learning algorithm developed by Oracle to...

Explicit Semantic Analysis in Oracle Machine Learning

BTS Sales Engagement Hub, Oracle Artificial Intelligence (AI)

Understanding text can be a difficult task for computers. While people can easily recognise the meaning behind words and phrases, computers need methods that assists them interpret language in a meaningful way. Explicit Semantic Analysis, also referred to as ESA, is a...

How is Singular Value Decomposition Implemented in Oracle Machine Learning

Oracle Artificial Intelligence (AI), BTS Sales Engagement Hub

Singular value decomposition is an unsupervised machine learning technique used primarily for feature extraction and the transformation of high dimensional data into a lower dimensional space whilst keeping meaningful data properties (dimensionality reduction). When...

Principal Component Analysis

BTS Sales Engagement Hub, Oracle Artificial Intelligence (AI)

As datasets continue to grow in size and complexity, analysing every variable can quickly become challenging. Many datasets contain hundreds or even thousands of features, some often provide similar information. Principal Component Analysis, also known as PCA, is an...
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