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:<math>\Theta=\frac{1}{\Pi} \sum^{\Pi}_{\pi=1}\sum^{M}_{m=1} D(q^{pi}_m(y|g^{\pi}_m(x))||p^{\pi}_m(f(x)|g^{\pi}_m(x)))</math>
where <math>D(Q||P)=\sum_iQ(i)\ln\frac{Q(i)}{P(i)}</math> is the [[Kullback–Leibler divergence]]. The combined minimization problem is optimized using a modified block gradient descent algorithm. For more information, see Wang et al.<ref>Wang, Shuhui et al. [https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6177671 S3MKL: Scalable Semi-Supervised Multiple Kernel Learning for Real-World Image Applications]. IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 14, NO. 4, AUGUST 2012</ref>▼
▲The combined minimization problem is optimized using a modified block gradient descent algorithm. For more information, see Wang et al.<ref>Wang, Shuhui et al. [https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6177671 S3MKL: Scalable Semi-Supervised Multiple Kernel Learning for Real-World Image Applications]. IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 14, NO. 4, AUGUST 2012</ref>
===Unsupervised learning===
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