S. Balasundaram

发表

A mixed finite element method for fourth order elliptic equations with variable coefficients

S. Balasundaram, P. K. Bhattacharyya, 1984 .

Active Set Fuzzy Support Vector ϵ-Insensitive Regression Approach

S. Balasundaram, R. Singh, Rampal Singha, 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

Application of Lagrangian Twin Support Vector Machines for Classification

S. Balasundaram, S. Balasundaram, N. Kapil, 2010, 2010 Second International Conference on Machine Learning and Computing.

L1-Norm Support Vector Regression in Primal Based on Huber Loss Function

S. Balasundaram, Anagha Puthiyottil, Yogendra Meena, 2019, Proceedings of ICETIT 2019.

A new approach in designing of local controlled curves and surfaces

S. Balasundaram, A. Punj, R. Govil, 1997 .

On pairing Huber support vector regression

Subhash Chandra Prasad, S. Balasundaram, 2020, Appl. Soft Comput..

On extreme learning machine for ε-insensitive regression in the primal by Newton method

Kapil, S. Balasundaram, bullet Kapil, 2013, Neural Computing and Applications.

Training primal twin support vector regression via unconstrained convex minimization

S. Balasundaram, Yogendra Meena, 2016, Applied Intelligence.

Robust twin support vector regression based on Huber loss function

S. Balasundaram, Subhash Chandra Prasad, Subhash Chandra Prasad, 2019, Neural Computing and Applications.

Training Lagrangian twin support vector regression via unconstrained convex minimization

S. Balasundaram, Deepak Gupta, S. Balasundaram, 2014, Knowl. Based Syst..

On optimization based extreme learning machine in primal for regression and classification by functional iterative method

S. Balasundaram, Deepak Gupta, S. Balasundaram, 2016, Int. J. Mach. Learn. Cybern..

On implicit Lagrangian twin support vector regression by Newton method

S. Balasundaram, Deepak Gupta, S. Balasundaram, 2014, Int. J. Comput. Intell. Syst..

Finite Newton method for implicit Lagrangian support vector regression

S. Balasundaram, Kapil, S. Balasundaram, 2011, Int. J. Knowl. Based Intell. Eng. Syst..

On Lagrangian support vector regression

S. Balasundaram, Kapil, S. Balasundaram, 2010, Expert Syst. Appl..

On Lagrangian L2-norm pinball twin bounded support vector machine via unconstrained convex minimization

S. Balasundaram, Subhash Chandra Prasad, S. Balasundaram, 2021, Inf. Sci..

A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization

S. Balasundaram, Deepak Gupta, Subhash Chandra Prasad, 2016, Applied Intelligence.

On Lagrangian twin support vector regression

S. Balasundaram, M. Tanveer, 2013, Neural Computing and Applications.

Robust Pinball Twin Bounded Support Vector Machine for Data Classification

Subhash Chandra Prasad, S. Balasundaram, P. Anagha, 2022, Neural Processing Letters.

Application of Extreme Learning Machine Method for Time Series Analysis

Ram Pal Singh, S. Balasundaram, S. Balasundaram, 2007 .

Robust Support Vector Regression in Primal with Asymmetric Huber Loss

S. Balasundaram, Yogendra Meena, 2018, Neural Processing Letters.

Lagrangian support vector regression via unconstrained convex minimization

S. Balasundaram, Deepak Gupta, Kapil, 2014, Neural Networks.

A new approach for training Lagrangian support vector regression

S. Balasundaram, Yogendra Meena, S. Balasundaram, 2016, Knowledge and Information Systems.

On robust twin support vector regression in primal using squared pinball loss

S. Balasundaram, P. Anagha, Yogendra Meena, 2018, J. Intell. Fuzzy Syst..

On a new approach for Lagrangian support vector regression

S. Balasundaram, Gagandeep Benipal, S. Balasundaram, 2018, Neural Computing and Applications.

Knowledge-based extreme learning machines

S. Balasundaram, D. Gupta, Deepak Gupta, 2016, Neural computing & applications (Print).

On optimization based extreme learning machine in primal for regression and classification by functional iterative method

S. Balasundaram, D. Gupta, S. Balasundaram, 2014, International Journal of Machine Learning and Cybernetics.

On existence of solution of the Dirichlet problem of fourth order partial differential equations with variable coefficients

S. Balasundaram, S. Balasundaram, P. K. Bhattacharyya, 1983 .

Smooth Newton method for implicit Lagrangian twin support vector regression

S. Balasundaram, Muhammad Tanveer, S. Balasundaram, 2013, Int. J. Knowl. Based Intell. Eng. Syst..

Object‐Oriented Data Modelling for Graphics Databases: a Declarative Approach

S. Balasundaram, Neelam Bhalla, S. Balasundaram, 1991, Comput. Graph. Forum.

Operations and queries in object-oriented databases supporting complex objects

S. Balasundaram, Neelam Bhalla, S. Balasundaram, 1993, Inf. Softw. Technol..

On a mixed finite element method for clamped anisotropic plate bending problems

S. Balasundaram, P. K. Bhattacharyya, S. Gopalsamy, 1989 .

Application of error minimized extreme learning machine for simultaneous learning of a function and its derivatives

S. Balasundaram, Kapil, S. Balasundaram, 2011, Neurocomputing.

1-Norm extreme learning machine for regression and multiclass classification using Newton method

S. Balasundaram, Deepak Gupta, Kapil, 2014, Neurocomputing.

Lagrangian support vector regression via unconstrained convex minimization

Kapil, S. Balasundaram, Deepak Gupta, 2014, Neural Networks.