how does the term m(mathbf d)$ arise as a result of a first order approximation which term arises as a result of a first order approximation and why it needs to be minimized? why is the term v(x  omega_i$) important in a work set what is the term v(mathbf d)$? how does the term $v(mathbf d)$ arise as a result of a first order approximation what term arises as a result of a first order approximation and why it needs to be minimized? when the term svm arises as a result of a first order approximation, and why it needs to be minimized, the svm should be trained on a first order approximation: when is $v(mathbf d)$ minimized how does the term omega_i$ arises as a result of a first order approximation? how does the term $v(mathbf d)$ arise as a result of a first order approximation and why it needs to be minimized? how does the term $V(mathbf d)$ arise as a result of a first order approximation, and why it needs to be minimized? svm definition how does svms training work what is the term of a first order approximation what is the term eigenvalues for the term eigenvalues why is the term $V(mathbf d)$ arises as a result of a first order approximation? when minimizing a first order approximation the term $v(mathbf d)$ arises as a result of how does the term V(mathbf d)$ arise from the description in 11.3.2 what is the v term in a svm method what the term of v(mathbf d)$ arises as a result of a first order approximation