Given a new continuous data vector z, determine which class it belongs to by determining whether the function is positive (1), zero (0) or negative (-1), i.
Given a new continuous data vector z, determine which class it belongs to by determining whether the function is positive (1), zero (0) or negative (-1), i.e., compute the sign function of (w dot z + b).
the vector to classify
Given a new discrete data vector z, determine which class it belongs to.
Given a new discrete data vector z, determine which class it belongs to.
the vector to classify
Determine the intercept b of the separating hyperplane w dot x + b = 0.
Determine the intercept b of the separating hyperplane w dot x + b = 0. Use ...
Determine the normal vector w of the separating hyperplane w dot x + b = 0.
Determine the normal vector w of the separating hyperplane w dot x + b = 0. Use a quadratic programming solver to find the optimal values for the the Lagrange multipliers a. Use these to compute w.
Return the fit (normal vector w, intercept b)
Show the flaw by printing the error message.
Show the flaw by printing the error message.
the method where the error occurred
the error message
Equality constraint to be satisfied, dot product of a and y == 0.
Equality constraint to be satisfied, dot product of a and y == 0.
the vector of Lagrange multipliers
Compute the Lagrangian Dual function, i.
Compute the Lagrangian Dual function, i.e., the objective function, to be maximized.
the vector of Lagrange multipliers
From the positive and negative cases (vectors), find an optimal separating hyperplane w dot x + b = 0.
From the positive and negative cases (vectors), find an optimal separating hyperplane w dot x + b = 0.
This class implements linear support vector machines (SVM). Each vector xi is stored as a row in the x matrix.