Article: Research conducted at F.B. Akoa and co-researchers has updated our knowledge about neural networks.

According to recent research from Douala, Cameroon, "Today, decomposition methods are one of the most popular methods for training support vector machines (SVMs). With the use of kernels that do not satisfy Mercer's condition, new techniques must be designed to handle nonpositive-semidefinite kernels resulting to this choice."

"In this work we incorporate difference of convex (DC functions) optimization techniques into de-composition methods to tackle this difficulty. The new approach needs no problem modification and we show that the only use of a truncated DC algorithms (DCAs) in the decomposition scheme produces a sufficient decrease of the objective function ...

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