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Machine Theory

Support vector machines : optimization based theory, by Naiyang Deng

By Naiyang Deng

''Preface help vector machines (SVMs), which have been brought via Vapnik within the early Nineteen Nineties, are proved potent and promising thoughts for information mining. SVMs have lately been breakthroughs upfront of their theoretical experiences and implementations of algorithms. they've been effectively utilized in lots of fields akin to textual content categorization, speech reputation, distant sensing photograph research, time series Read more...

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Extra resources for Support vector machines : optimization based theory, algorithms, and extensions

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Obviously, the second-order cone is a proper cone. 23), the proper cones can be specified as the second-order cones. This leads to the following definition. t. 42) where c ∈ Rn , A ∈ Rp×n , b ∈ Rp , A¯i ∈ Rmi ×n , ¯bi ∈ Rmi , i = 1, · · · , m, and Lmi is a second-order cone in Rmi , mi is a positive integer, i = 1, · · · , m. 42), we need the following theorem. e. Lm = Lm ∗ . Proof For the case m = 1, the conclusion is obvious. So we need only 2. In fact, on one hand, taking any u = to show Lm = Lm ∗ when m (u1 , u ¯T )T ∈ Lm , for any v = (v1 , v¯T )T ∈ Lm , we have (u · v) = u1 v1 + (¯ u · v¯) u1 v1 − u ¯ by Cauchy-Schwarz inquality.

Proof For the case m = 1, the conclusion is obvious. So we need only 2. In fact, on one hand, taking any u = to show Lm = Lm ∗ when m (u1 , u ¯T )T ∈ Lm , for any v = (v1 , v¯T )T ∈ Lm , we have (u · v) = u1 v1 + (¯ u · v¯) u1 v1 − u ¯ by Cauchy-Schwarz inquality. Therefore u ∈ L Lm ⊆ Lm ∗ . 44) Optimization 29 On the other hand, taking any u = (u1 , u ¯T )T ∈ Lm ∗ , for any v = (v1 , v¯T )T , we have (u · v) 0. 45) by examining two different cases. 46) thus v1 0 since v ∈ Lm . e. 45) is true. 45) and so Lm ⊇ Lm ∗ .

7) satisfying Slater’s condition. 19). 3. Now it will be extended from the case with usual inequality constraints to the one with generalized inequality constraints [17]. 1 (Cone and convex cone) A set K in Rn is called a cone if for every x ∈ K and λ 0, λx ∈ K. A set K in Rn is called a convex cone if it is a cone and a convex set, which means that for any u, v ∈ K and λ1 , λ2 0, λ1 x1 + λ2 x2 ∈ K. 2 (Proper cone) A set K in Rn is called a proper cone if it satisfies: (i) K is a convex cone; (ii) K is closed; (iii) K is solid, which means it has nonempty interior; (iv) K is pointed, which means that it contains no line (or, equivalently, x must be null (x = 0) if x ∈ K and −x ∈ K).

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