2011年1月7日 · For soft-margin SVM, it's easer to explain them in terms of dual variables. Your support vector predictor in terms of dual variables is the following function. Here, alphas and b …

2021年7月8日 · This formulation - which in literature identifies the optimization problem for a soft margin classifier - makes it work also for non-linearly separable datasets and introduces: …

2018年2月15日 · I'm trying to solve the SVM from primal, by minimizing this: The derivative of J wrt w is (according to the reference above): So this is using the "hinge" loss, and C is the …

2015年7月4日 · Yes, both the soft and hard formulations of standard SVM are convex optimization problems, hence have unique global optima. I suppose if the problem is incredibly …

2021年4月15日 · I am implementing soft margin svm with cvxpy. I keep getting the error ValueError: setting an array element with a sequence.

The "primal" form of the soft-margin SVM model (i.e. the definition above) can be converted to a "dual" form. I did that, and I am able to get the Lagrange variable values (in the dual form). …

The optimal decision surface is orthogonal to that line and intersects it at the halfway point. Therefore, it passes through . So, the SVM decision boundary is: Working algebraically, with …

2015年10月13日 · I understand that in SVM (Support Vector Machines) you can add a slack variable ε to "soften" the borders. Reading from some textbooks, it mentions that ε > 1 will …

2016年3月13日 · In order to train SVM you need binary labeled data - usually with "+1" and "-1", you train the model and later on use the equation provided to assign predicted labels to each …

2021年11月4日 · I agree that there is no hard-margin SVM in scikit-learn. However, I don't think that C=1e-10 is the correct numerical way to create hard SVM. We should set up large C to …