BUAL 5610 L17: Support Vector Machines (Quiz 2)

1 / 14
separable hyperplanes
Click the card to flip 👆
Terms in this set (14)
to transform data, we choose a _____ functionKernelcommon kernel functions- linear - polynomial - radial basis - sigmoidpros of support vector regression (?)- effective in high dimensional spaces (many variables) - uses a subset of training points in the decision function (called support vectors), so it's also memory efficientcons of support vector regression (?)-sensitive to noise- a small number of mislabeled examples can dramatically decrease the performance - selecting the right kernel is not an easy task - gets slower when dataset size is bigger - it classifies through geometry whereas a lot of classification problems probability gives better results