Quantum support vector machine based on regularized Newton method
R Zhang, J Wang, N Jiang, H Li, Z Wang - Neural Networks, 2022 - Elsevier
An elegant quantum version of least-square support vector machine, which is exponentially
faster than the classical counterpart, was given by Rebentrost et al. using the matrix …
faster than the classical counterpart, was given by Rebentrost et al. using the matrix …
Sparse LSSVM in primal using Cholesky factorization for large-scale problems
S Zhou - IEEE transactions on neural networks and learning …, 2015 - ieeexplore.ieee.org
For support vector machine (SVM) learning, least squares SVM (LSSVM), derived by duality
LSSVM (D-LSSVM), is a widely used model, because it has an explicit solution. One obvious …
LSSVM (D-LSSVM), is a widely used model, because it has an explicit solution. One obvious …
Training Lagrangian twin support vector regression via unconstrained convex minimization
S Balasundaram, D Gupta - Knowledge-Based Systems, 2014 - Elsevier
In this paper, a new unconstrained convex minimization problem formulation is proposed as
the Lagrangian dual of the 2-norm twin support vector regression (TSVR). The proposed …
the Lagrangian dual of the 2-norm twin support vector regression (TSVR). The proposed …
A new approach for training Lagrangian twin support vector machine via unconstrained convex minimization
S Balasundaram, D Gupta, SC Prasad - Applied Intelligence, 2017 - Springer
In this paper, a novel unconstrained convex minimization problem formulation for the
Lagrangian dual of the recently introduced twin support vector machine (TWSVM) in simpler …
Lagrangian dual of the recently introduced twin support vector machine (TWSVM) in simpler …
A new fuzzy support vector machine with pinball loss
The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value
based on its relevance, making it less sensitive to noise or outliers in the data. Although …
based on its relevance, making it less sensitive to noise or outliers in the data. Although …
New smoothing SVM algorithm with tight error bound and efficient reduced techniques
The quadratically convergent algorithms for training SVM with smoothing methods are
discussed in this paper. By smoothing the objective function of an SVM formulation, Lee and …
discussed in this paper. By smoothing the objective function of an SVM formulation, Lee and …
A New enhanced Fuzzy Support Vector Machine with Pinball Loss
RN Verma, R Srivastava, N Subbarao, GP Singh - 2022 - researchsquare.com
The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value
based on its relevance, making it less sensitive to noise or outliers in the data. Although …
based on its relevance, making it less sensitive to noise or outliers in the data. Although …
[PDF][PDF] Discover Artificial Intelligence
RN Verma, R Deo, R Srivastava, N Subbarao… - Discover, 2023 - researchgate.net
The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value
based on its relevance, making it less sensitive to noise or outliers in the data. Although …
based on its relevance, making it less sensitive to noise or outliers in the data. Although …
Which is better? Regularization in RKHS vs R^ m on Reduced SVMs
S Zhou - Statistics, Optimization & Information Computing, 2013 - iapress.org
In SVMs community, the learning results are always a combination of the selected functions.
SVMs have two mainly regularization models to solve the combination coefficients. The most …
SVMs have two mainly regularization models to solve the combination coefficients. The most …