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Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification
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DOI：10.1007/s13042-020-01235-y.png)
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In this paper, we suggest a novel approach termed as regularized based implicit Lagrangian twin extreme learning machine in primal as a pair of unconstrained convex minimization problem (RILTELM) where regularization term is added to follow the structural risk minimization principle. Here, we consider 2-norm of the slack vector of variables to make the problem strongly convex which results in a unique solution. Since it has non-smooth plus functions in their objective function, so we find an approximate solution by replacing the non-smooth plus function with smooth approximation function because to find an approximation solution in primal space is always superior to its dual. Due to non-smooth plus function, we solve the problem by either smooth approximation approach or generalized derivative approach. In addition, a functional iterative scheme is also suggested to find the optimal solution. Hence, no external optimization toolbox is required unlike in twin extreme learning machine (TELM) and twin support vector machine (TWSVM). The numerical experiments are demonstrated on artificial and real-world datasets and compared with TWSVM, ELM, TELM and LSTELM to establish the efficacy and applicability of proposed RILTELM.
Keyword:
Support vector machine
Extreme learning machine
Twin extreme learning machine
Smoothing approaches
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
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