View Full Version : Homework 8

  1. Including a lecture video segment
  2. Using TeX in your post
  3. Libsvm
  4. scale or not
  5. HW 8, A case of data being hopelessly inseparable?
  6. libsvm convergence
  7. K-means clustering for RBF centers
  8. SVMs versus NNs
  9. libsvm and Python
  10. What is the size of the downloaded data set?
  11. meaning of gamma in polynomial kernel
  12. Why is CVXOPT slower than LIBSVM?
  13. Should SVMs ALWAYS converge to the same solution given the same data?
  14. Signficance Of High Number Of Iterations
  15. Intensity and Symmetry
  16. RBF - distance and weights
  17. Libsvm and Octave on Windows
  18. Sampling and averaging for training/validation?
  19. Why not use soft margin SVM everytime?
  20. SMO algorithm
  21. can't get LIBSVM on Ubuntu, how to scale SV?
  22. Cross validation parameter selection
  23. SVM classifier One-vs-One and One-Vs-All clarification
  24. on the right track?
  25. example of effects of C
  26. Understanding Q4
  27. How to disable the output of svm-train in Python
  28. How many support vectors is too many?
  29. structure 'svm_node' in libsvm.
  30. scale=FALSE
  31. SVM and LIONsolver
  32. Probability estimate from soft margin SVMs
  33. libsvm random seeding (Q7)
  34. Q1 Quadratic Programming
  35. Q7 & Q8 - which data to use
  36. Q5 two answers?
  37. computing w, b for soft margin SVM
  38. Kernel methods for SVM and quantum computing
  39. Hard margin on using LIBSVM : C parameter?
  40. *ANSWER* hw8 q7
  41. Learning performance comparable to SVM that doesnt require QP
  42. HW8 Q2-6 Converting digit data to libsvm format
  43. *Answer* checking the answer of Q5
  44. *Answer* a second answer for Q6?
  45. *Answer* checking the answer of Q7
  46. Q1 - I don't get the answer
  47. Q2 - Classifier correctly predicts only a few classes