Great Video, but I did not understand the part where d2 = 1.5 d1 + 0.5 d1 ^2. Is this just a random expression that you considered or am I missing something? Your response will be much appreciated! Thanks in advance! :)
The original feature space is x, which has d1 dimensions. The transformed (expanded feature space) is phi(x). The transformation phi can take several forms. In this video, I considered a quadratic transformation. In this case, the number of dimensions of phi(x) is d2. There is a relation between d2 and d1, which is d2 = 1.5 d1 + 0.5 d1 ^2. But do not forget that this relation applies only if the transformation phi is quadratic. Based on the nature of the transformation phi, the relation between d1 and d2 can be mathematically derived. Hope this helps.
Hi, I just try a 3D modelling software with radial basis functions. Is it same with this kernel that you explain? I've no statistic background, just try to understand briefly.
Hi, kamal! 5149 because in the dot product of two vectors, the number of additions = number of multiplications - 1 = 5150 - 1 = 5149. If you don't get it, consider two vectors a and b such that a = [a1, a2, a3, a4] and b = [b1, b2, b3, b4]. Their dot product is equal to: a.b = a1×b1 + a2×b2 + a3×b3 + a4×b4. The number of multiplications is 4 (which is equal to the dimension of a and b), and the number of additions is (4-1) = 3.
Dear professor, I am writing this E-mail requesting your help to get some further explanations concerning the article you published:(LSSVM based initialization approach for parameter estimation of dynamical systems), [doi:10.1088/1742-6596/490/1/012004 ] My best regards.
Finally I found a good explanation for kernels in svm which helps .. thanks a lot
Probably the first time I really understand this now. Thanks alot sir!
Happy to help Mr. Domien
very clear explanation.. please create more content.. couldnt thank you enough
Thanks for your nice words. Wish you all the best.
I can't thank you enough for this great explanation! you saved me a looooot of time!
I'm happy to hear that! best of luck! :)
Great Video, but I did not understand the part where d2 = 1.5 d1 + 0.5 d1 ^2. Is this just a random expression that you considered or am I missing something? Your response will be much appreciated! Thanks in advance! :)
The original feature space is x, which has d1 dimensions. The transformed (expanded feature space) is phi(x). The transformation phi can take several forms. In this video, I considered a quadratic transformation. In this case, the number of dimensions of phi(x) is d2. There is a relation between d2 and d1, which is d2 = 1.5 d1 + 0.5 d1 ^2. But do not forget that this relation applies only if the transformation phi is quadratic. Based on the nature of the transformation phi, the relation between d1 and d2 can be mathematically derived. Hope this helps.
Thanks for the video! Can you please point me to the derivation of operation number =1.5d1 + 0.5 sqr(d1) ? Thank you!
Hi, I just try a 3D modelling software with radial basis functions. Is it same with this kernel that you explain? I've no statistic background, just try to understand briefly.
Hello, first of all thank you so much for this! Can you tell me where you obtained the 5149 additions from? I don't understand how it happened
Hi, kamal! 5149 because in the dot product of two vectors, the number of additions = number of multiplications - 1 = 5150 - 1 = 5149. If you don't get it, consider two vectors a and b such that a = [a1, a2, a3, a4] and b = [b1, b2, b3, b4]. Their dot product is equal to: a.b = a1×b1 + a2×b2 + a3×b3 + a4×b4. The number of multiplications is 4 (which is equal to the dimension of a and b), and the number of additions is (4-1) = 3.
Dear professor,
I am writing this E-mail requesting your help to get some further explanations concerning the article you published:(LSSVM based initialization approach for parameter estimation of dynamical systems), [doi:10.1088/1742-6596/490/1/012004 ]
My best regards.
Thanks a lot Sir..
what is the alpha term (alpha_i)? I missed this part..
It's the largangian coefficient, a variable that if optimized allows to easily compute parameters w, b.