3 ms·

Potentially stupid question: What is an SVM? The linked page uses the acronym almost 30 times but doesn't define it.

lainon 9y ago
Support Vector Machine
visarga 9y ago
I know how it feels. SVM's are an old and tried classification method that was in fashion before the DL craze. > An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. One advantage of SVM's is that they don't use all data points to decide on the separation plane, just the closest points to the gap (the support vectors), making it more invariant. Another advantage is that they can efficiently perform a non linear classification using the kernel trick, implicitly mapping their inputs into high dimensional feature spaces (here kernel means a distance function between two data points). https://en.wikipedia.org/wiki/Support_vector_machine https://en.wikipedia.org/wiki/Support_vector_machine https://en.wikipedia.org/wiki/Positive-definite_kernel https://en.wikipedia.org/wiki/Positive-definite_kernel I recommend Andrew Ng's ML course for learning about SVM's.
abhgh 9y ago
Support Vector Machine. A Machine Learning technique that's used typically for classification and regression, but also has been adapted to novelty detection, stuctured prediction, ranking etc. I wrote up a beginner's tutorial some time back, if you are interested [1]. [1] https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93 https://blog.statsbot.co/support-vector-machines-tutorial-c1...
- roumenguha 9y ago
  Great read, thanks!
make3 9y ago
I don't want to be too much of an asshole but don't you have Google like the rest of us. I always think it's so weird when people ask something to the comments in HN of all places when they could literally just have textually written it in Wikipedia or wtv and gotten a better answer
- xcambar 9y ago
  Congratulations, you made it! You are now, by your own words, too much of an asshole. What an achievement! Please allow me to explain my reasoning: I'm of those with no clue about the meaning of SVM, and I came to the comments section looking for an answer to the question "what the ... is SVM anyway?". Even though I know the way to Google (https://duckduckgo.com https://duckduckgo.com, right?), I'm happy someone asked this question and another answered it and as a consequence saved me (and hopefully others) a couple of clicks and taught me something that I might have otherwise been too lazy to investigate myself. Should I want to be picky, I'd even suggest that having the question answered in the comment section is environmentally more efficient than your suggestion that we should all open a new tab and do the same research. Save a tree, answer the not-so-obvious questions "for the masses".
  - dr_zoidberg 9y ago
    Not only saved a couple of clicks, bu there were some good resources thrown around. When trying to learn a new topic without much knowledge, the most difficult part is finding good reads, videos, tutorials, etc, that will set you on track.
manojlds 9y ago
Before I joined the ML team at my company I too had no clue. Now, it is what we talk about 30% of the time :)

Support Vector Machine

I know how it feels. SVM's are an old and tried classification method that was in fashion before the DL craze. > An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. One advantage of SVM's is that they don't use all data points to decide on the separation plane, just the closest points to the gap (the support vectors), making it more invariant. Another advantage is that they can efficiently perform a non linear classification using the kernel trick, implicitly mapping their inputs into high dimensional feature spaces (here kernel means a distance function between two data points). https://en.wikipedia.org/wiki/Support_vector_machine https://en.wikipedia.org/wiki/Support_vector_machine https://en.wikipedia.org/wiki/Positive-definite_kernel https://en.wikipedia.org/wiki/Positive-definite_kernel I recommend Andrew Ng's ML course for learning about SVM's.

abhgh 9y ago

Support Vector Machine. A Machine Learning technique that's used typically for classification and regression, but also has been adapted to novelty detection, stuctured prediction, ranking etc. I wrote up a beginner's tutorial some time back, if you are interested [1]. [1] https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93 https://blog.statsbot.co/support-vector-machines-tutorial-c1...

roumenguha 9y ago
Great read, thanks!

make3 9y ago

I don't want to be too much of an asshole but don't you have Google like the rest of us. I always think it's so weird when people ask something to the comments in HN of all places when they could literally just have textually written it in Wikipedia or wtv and gotten a better answer

xcambar 9y ago
Congratulations, you made it! You are now, by your own words, too much of an asshole. What an achievement! Please allow me to explain my reasoning: I'm of those with no clue about the meaning of SVM, and I came to the comments section looking for an answer to the question "what the ... is SVM anyway?". Even though I know the way to Google (https://duckduckgo.com https://duckduckgo.com, right?), I'm happy someone asked this question and another answered it and as a consequence saved me (and hopefully others) a couple of clicks and taught me something that I might have otherwise been too lazy to investigate myself. Should I want to be picky, I'd even suggest that having the question answered in the comment section is environmentally more efficient than your suggestion that we should all open a new tab and do the same research. Save a tree, answer the not-so-obvious questions "for the masses".
- dr_zoidberg 9y ago
  Not only saved a couple of clicks, bu there were some good resources thrown around. When trying to learn a new topic without much knowledge, the most difficult part is finding good reads, videos, tutorials, etc, that will set you on track.

manojlds 9y ago

Before I joined the ML team at my company I too had no clue. Now, it is what we talk about 30% of the time :)