Linear svm
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Linear SVM: A Case Study, First Part (Coding And Theory) discusses spam filtering using a linear support vector machine (SVM) model. It introduces the concepts of term frequency-inverse document frequency (TF-IDF) to transform text into numeric vectors for modeling, defines the separating hyperplane and role of bias terms in linear SVMs, and explains that the goal is to find the hyperplane that maximizes the margin between examples of different classes for classification.
Linear svm
- 1. Linear SVM: A Case Study, First Part ( Coding And Theory) ANANDA SWARUP DAS, TECHNICAL STAFF MEMBER, KNOWLEDGE ENGINEERING AND ENCRYPTED ANALYTICS TEAM, IBM INDIA RESEARCH LABS, NEW DELHI.
- 2. Spam and Ham How I read the data: Using Pandas How My Data Looks:
- 3. Processing the Text Number of Classes in My Data Working with Text Data
- 4. TF-IDF (Term Frequency-Inverse Document Frequency Vectors) Output
- 5. TfidfTranformer and Our Purpose Objective: TfidfTransformer:
- 6. Hyperplane Definition (Theory for Linear SVM Begins)
- 7. Role of the bias
- 8. The hyperplane that we want A Criteria for Linear SVM to work
- 9. Linear SVM and the Decision Rules
- 10. Orthogonality of the Weight Vector
- 14. The Concept of Margin
- 15. Why Margin is Expressed in terms of the unit vector