Solved Support Vector Machine | Non-Linear SVM Example by Mahesh Huddar

Solved Support Vector Machine | Non-Linear SVM Example by Mahesh Huddar
Support Vector Machine: • Support Vector Machine...
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[@athulmathewkonoor]

Good explanation. Great place to refresh before an exam in 20 mins.

[@mithunchandrasaha403]

Very Nice Explanation,Sir. Needs More Tutorial From You.

[@MaheshHuddar]

Welcome
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[@shahzadfaisal1505]

Thank you sir for these great lectures. I would request if you please upload SVM kernel functions along with thei explanation and examples like polynomial, RBF, Gaussian, Hyerbolic tagent etc with examples.

[@MaheshHuddar]

Welcome
I am preparing the videos
Will upload soon

[@shahzadfaisal1505]

@@MaheshHuddar Thank you sir. Best wishes

[@subrahmanyamkesani7304]

hi, Can you please answer my query.
In the result of hyper plane, intercept is -3. where as in the graph intercept is +3. Please explain how ?

[@arshraina6725]

If you go into the theory b is not the intercept, it is the offset. Hencs, -ve leads to right shift

[@vigneshlokesh145]

Crystal clear.. thank you sir

[@just_timepass_things-foryou]

easy
explanation

[@durgeshsharma1890]

Explained very nicely.
Please keep it up like this....

[@MaheshHuddar]

Thank You
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[@mvikyk]

Thank you. Kindly clarify two points 1) How the transformation function was determined 2) How hyperlane is drawn from (1,1) and bias=-3

[@bensonkayden9346]

Instablaster...

[@Adeel_Noshahi]

I think transformation function is given

[@maliksheri4865]

Thanks Brother
Its Very Informative.

[@chiragsharma6261]

What if instead of (2,2) there are points like (3,3) , (-3,3) , ( 3,-3) and (-3,-3)....what will be the conditions for that?

[@franktimestamp]

Very clear explanations...

[@MaheshHuddar]

Thank You
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[@syedmuhammadrafay228]

JazakAllah

[@MaheshHuddar]

Welcome
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[@subhankarbhattacharya2940]

Is this problem complete ? Dont we have to transform it to the original non linear space and show the non linear boundary ?

[@arunkumark4828]

Good sir, but is it a predetermined function used in svm like explained in 4x

[@venkatadineshjeedigunta510]

Good explanation Sir , one question though.. , how did you arrive on the equations to convert data from feature space to another ?.. are these standard equations ? ,please clarify

[@kushagrashukla8418]

Actually that is kernel function you will be provided with it in numericals, however I can let u ponder over it why we got it so. Listen carefully at 1:23 see you feel a circle is hyperplane that having r=2 centered at origin now clearly we want to transform it via kernel as per SVM classification. So we tried transforming all positive(blue) points with the first branch of theta function at 2:58 however negative points were remain untouched since it falls inside the circle hyperplane. Now clearly we get a picture as 4:23. Hence the question is again similar to linear case and you are done !!

[@gangsofsouth]

@@kushagrashukla8418 too late for him 😀, though very helpful for me, today only have AI in manufacturing paper.

[@dp8837]

@@kushagrashukla8418 thanks sir loved the reply

[@S-tz6lb]

@@kushagrashukla8418 sir, please help me. the bias is -3 but he plotted it in +3 why?

[@ayushrajput5327]

Thank you sir

[@navyamr8455]

Thank you sir...

[@HarshPrajapati_1]

Good explanation

[@MaheshHuddar]

Thanks and welcome
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[@narimenehindarar3857]

thank you very much ^^

[@MaheshHuddar]

Welcome
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[@sariyaansari4096]

Dear Mahesh Sir,
How did we get this formula 4-x2 + |x1-x2|?
The number "4" is fixed or not?
Is number "4" representing total number of points dataset that 4 positive and 4 negative. If this is the case how will we handle if positive points are 4 and negative are 2?
Please explain, if possible

[@kushagrashukla8418]

Actually that is kernel function you will be provided with it in numericals, however I can let u ponder over it why we got it so. Listen carefully at 1:23 see you feel a circle is hyperplane that having r=2 centered at origin now clearly we want to transform it via kernel as per SVM classification. So we tried transforming all positive(blue) points with the first branch of theta function at 2:58 however negative points were remain untouched since it falls inside the circle hyperplane. Now clearly we get a picture as 4:23. Hence the question is again similar to linear case and you are done !!

[@sanidhyajain1828]

thank you!

[@MaheshHuddar]

Welcome
Do like share and subscribe

[@namanjain8458]

how did you find the mapping function?

[@kushagrashukla8418]

Actually that is kernel function you will be provided with it in numericals, however I can let u ponder over it why we got it so. Listen carefully at 1:23 see you feel a circle is hyperplane that having r=2 centered at origin now clearly we want to transform it via kernel as per SVM classification. So we tried transforming all positive(blue) points with the first branch of theta function at 2:58 however negative points were remain untouched since it falls inside the circle hyperplane. Now clearly we get a picture as 4:23. Hence the question is again similar to linear case and you are done !!

[@tekken1935]

How to decide whether to take 2 or 3 points for consideration. For linear example, 3 points s1,s2,s3 were taken. For non linear example, only s1 and s2 were taken. I have tried to solve this question by taking 3 points, but it does not work out well, please tell how to decide how many number of points to consider when solving.

[@vineethkrishna9216]

I'm having my exam tommorow , can you pls tell me if you remember the ans for your question which I've replied to right now ?

[@tekken1935]

@@vineethkrishna9216 Better to take 3 points.

[@vineethkrishna9216]

@@tekken1935 tysm

[@kushagrashukla8418]

You ar esuppose to take only support vectors, points nearest to SVM plane on either side, here we have just two not three unlike Linear case.

[@bugresolver4265]

how does the formula change if we have more than 2 points sir please answer this query

[@madhusudhanansambath6227]

also suggest ML book for more numerical problem of all ml algorithms

[@firdaousziani3568]

On what basis did you choose s1 ,s2

[@ramnarayan3323]

Nice ..

[@MahyunSubaedin-ff4ik]

may i know the reference of the formula, sir, like journal or thesis or maybe book? im afraid my teacher gonna ask me

[@akshathabs5322]

How v can plot for three points?

[@mohamedzaini2899]

What equation sir

[@madhusudhanansambath6227]

Can you please send book name for this problem

[@gsashish2618]

How do we select positive and negative data points

[@gsashish2618]

@@MaheshHuddar sir in few other problems the points were normally given and asked to find out the hyper plane

[@MuhammadAhmed-ij9ow]

@@gsashish2618 Then its linear SVM problem. The method to solve them is a little different. search that on youtube.

[@subhankarbhattacharya2940]

How do we know, what transformation to use?

[@kushagrashukla8418]

Actually that is kernel function you will be provided with it in numericals, however I can let u ponder over it why we got it so. Listen carefully at 1:23 see you feel a circle is hyperplane that having r=2 centered at origin now clearly we want to transform it via kernel as per SVM classification. So we tried transforming all positive(blue) points with the first branch of theta function at 2:58 however negative points were remain untouched since it falls inside the circle hyperplane. Now clearly we get a picture as 4:23. Hence the question is again similar to linear case and you are done !!

[@mysterynights6255]

@@kushagrashukla8418 circle kaa equation nahi use kiya haii yrr eqn khaa seee aya haii yeah ptha hai tooh bthaoo?

[@dr.shambhujha3999]

Why 1 as a bias term to be added

[@study_loif1915]

Bias value is 1

[@Popumalu]

Sir can you please share this ppt

[@vaiebhavpatil2340]

BASED

[@MaheshHuddar]

Based...?

[@vaiebhavpatil2340]

@@MaheshHuddar I mean it's good

[@vaiebhavpatil2340]

@@MaheshHuddar Massachusetts Institute of Technology

[@mohamedzaini2899]

Formula explain sir