Accelerated Stochastic Gradient Method for Support Vector Machines Classification with Additive Kernel
| Title | Accelerated Stochastic Gradient Method for Support Vector Machines Classification with Additive Kernel |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Wang, X., Zhou, S. |
| Conference Name | 2017 First International Conference on Electronics Instrumentation Information Systems (EIIS) |
| Keywords | accelerated mini-batch stochastic gradient descent algorithm, accelerated stochastic gradient method, Acceleration, acceleration strategy, additive kernel, additive kernel version, Additives, Classification algorithms, composability, data mining, gradient approximation, gradient methods, Kernel, learning (artificial intelligence), linear classifier, machine learning, Metrics, mini-batch stochastic gradient descent, Nesterov's acceleration strategy, pattern classification, polynomials, pubcrawl, resilience, Resiliency, Stochastic processes, Support vector machines, Support vector machines (SVMs), support vector machines classification, SVM classification, Testing, Training |
| Abstract | Support vector machines (SVMs) have been widely used for classification in machine learning and data mining. However, SVM faces a huge challenge in large scale classification tasks. Recent progresses have enabled additive kernel version of SVM efficiently solves such large scale problems nearly as fast as a linear classifier. This paper proposes a new accelerated mini-batch stochastic gradient descent algorithm for SVM classification with additive kernel (AK-ASGD). On the one hand, the gradient is approximated by the sum of a scalar polynomial function for each feature dimension; on the other hand, Nesterov's acceleration strategy is used. The experimental results on benchmark large scale classification data sets show that our proposed algorithm can achieve higher testing accuracies and has faster convergence rate. |
| URL | https://ieeexplore.ieee.org/document/8298732/ |
| DOI | 10.1109/EIIS.2017.8298732 |
| Citation Key | wang_accelerated_2017 |
- machine learning
- Training
- testing
- SVM classification
- support vector machines classification
- Support vector machines (SVMs)
- Support vector machines
- Stochastic processes
- Resiliency
- resilience
- pubcrawl
- polynomials
- pattern classification
- Nesterov's acceleration strategy
- mini-batch stochastic gradient descent
- Metrics
- accelerated mini-batch stochastic gradient descent algorithm
- linear classifier
- learning (artificial intelligence)
- Kernel
- gradient methods
- gradient approximation
- Data mining
- composability
- Classification algorithms
- Additives
- additive kernel version
- additive kernel
- acceleration strategy
- Acceleration
- accelerated stochastic gradient method