Machine Learning 1: UNIT-5(A) PPTs: Dimensionality Reduction PPTs
Unit V (A)
Dimensionality Reduction
1. The Curse of Dimensionality
2. Main Approaches for Dimensionality Reduction
3. Projection
4. PCA
Machine Learning 1: UNIT 3 : Support Vector Machines MCQs
UNIT 3 Support Vector Machines MCQs ----------------------------------------------------------------------------------------------------------------------------- 1. A Support Vector Machine can be used for A. Performing linear or nonlinear classification B. Performing regression C. For outlier detection D. All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A. Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called Soft margin classification Hard margin classification Strict margin classification Loose margin classification Ans...
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