Question

Constrained optimization: Imposing norm constrain on input to find small solutions

score 87 · Answer 1 · 2026-03-25 20:08:12

87

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Constrained optimization: Imposing norm constrain on input to find small solutions

Published on 25 Mar 2026 - 20:08

score 208 · Answer 2 · 2026-03-29 00:06:41

208

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Gradient-based optimization: A small change in the input obtains a corresponding change in the output

Published on 29 Mar 2026 - 0:06

score 306 · Answer 3 · 2019-09-30 02:39:11

306

Views

Conditional Probability and KL Divergence

Published on 30 Sep 2019 - 2:39

score 57 · Answer 4 · 2019-10-01 06:00:13

57

Views

Proof to find C in isomap

Published on 01 Oct 2019 - 6:00

score 90 · Answer 5 · 2026-03-25 09:25:31

90

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Diversity of a Set & Hypercube Vertices related to Statistical Machine Learning

Published on 25 Mar 2026 - 9:25

score 181 · Answer 6 · 2026-03-25 06:31:41

181

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Physical Meaning Behind Matrix Factorization

Published on 25 Mar 2026 - 6:31

score 138 · Answer 7 · 2014-03-16 04:45:01

138

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What is the rationale behind ROC curves?

Published on 16 Mar 2014 - 4:45

score 1.2k · Answer 8 · 2026-03-27 18:35:40

1.2k

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What is the most general formalism for machine learning?

Published on 27 Mar 2026 - 18:35

score 2.3k · Answer 9 · 2026-03-25 09:22:11

2.3k

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Understanding Regularization parameters in Machine Learning/Statistics

Published on 25 Mar 2026 - 9:22

score 110 · Answer 10 · 2014-03-19 02:39:46

110

Views

SVM primal formulation, does the constants constraints matter/

Published on 19 Mar 2014 - 2:39

score 84 · Answer 11 · 2026-03-25 06:26:07

84

Views

Normalizing multiple different features from unknown distributions

Published on 25 Mar 2026 - 6:26

score 679 · Answer 12 · 2026-03-26 02:54:59

679

Views

Given a set of data points, how to use gradient descent to find the minimum in the function that passes from those data points?

Published on 26 Mar 2026 - 2:54

score 109 · Answer 13 · 2014-03-19 19:42:03

109

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Why is the constraint for SVM with offset the way it is? $\sum^{n}_{t=1}\alpha_ty^{(t)} = 0$

Published on 19 Mar 2014 - 19:42

score 1.9k · Answer 14 · 2014-03-19 20:01:39

1.9k

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How do you compute the offset parameter for an SVM from the dual solution?

Published on 19 Mar 2014 - 20:01

score 38 · Answer 15 · 2014-03-20 00:31:21

Why do polynomial sequences have the coefficient $\sqrt2$ in front of them as in $\phi(x) = [x_1, x_2, \sqrt2x_1x_2, x_1^2, x_2^2]$

List Question

Constrained optimization: Imposing norm constrain on input to find small solutions

Gradient-based optimization: A small change in the input obtains a corresponding change in the output

Conditional Probability and KL Divergence

Proof to find C in isomap

Diversity of a Set & Hypercube Vertices related to Statistical Machine Learning

Physical Meaning Behind Matrix Factorization

What is the rationale behind ROC curves?

What is the most general formalism for machine learning?

Understanding Regularization parameters in Machine Learning/Statistics

SVM primal formulation, does the constants constraints matter/

Normalizing multiple different features from unknown distributions

Given a set of data points, how to use gradient descent to find the minimum in the function that passes from those data points?

Why is the constraint for SVM with offset the way it is? $\sum^{n}_{t=1}\alpha_ty^{(t)} = 0$

How do you compute the offset parameter for an SVM from the dual solution?

Why do polynomial sequences have the coefficient $\sqrt2$ in front of them as in $\phi(x) = [x_1, x_2, \sqrt2x_1x_2, x_1^2, x_2^2]$

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