Analytical Incremental Learning: Fast Constructive Learning Method for Neural Network
نویسندگان
چکیده
13:20 13:40 Analytical Incremental Learning: Fast Constructive Learning Method for Neural Network Syukron Ishaq Alfarozi, Noor Akhmad Setiawan, Teguh Bharata Adji, Kuntpong Woraratpanya, Kitsuchart Pasupa, Masanori Sugimoto Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada Hokkaido University
منابع مشابه
A Hybrid Framework for Building an Efficient Incremental Intrusion Detection System
In this paper, a boosting-based incremental hybrid intrusion detection system is introduced. This system combines incremental misuse detection and incremental anomaly detection. We use boosting ensemble of weak classifiers to implement misuse intrusion detection system. It can identify new classes types of intrusions that do not exist in the training dataset for incremental misuse detection. As...
متن کاملOutput partitioning of neural networks
Many constructive learning algorithms have been proposed to find an appropriate network structure for a classification problem automatically. Constructive learning algorithms have drawbacks especially when used for complex tasks and modular approaches have been devised to solve these drawbacks. At the same time, parallel training for neural networks with fixed configurations has also been propo...
متن کاملConstructive Methods for a New Classifier Based on a Radial-Basis-Function Neural Network Accelerated by a Tree
We present a new constructive algorithm for building Radial-Basis-Function (RBF) network classiiers and a tree based associated algorithm for fast processing of the network. This method, named Constructive Tree Radial-Basis-Function (CTRBF), allows to build and train a RBF network in one pass over the training data set. The training can be in supervised or unsupervised mode. Furthermore, the al...
متن کاملTwo Novel Learning Algorithms for CMAC Neural Network Based on Changeable Learning Rate
Cerebellar Model Articulation Controller Neural Network is a computational model of cerebellum which acts as a lookup table. The advantages of CMAC are fast learning convergence, and capability of mapping nonlinear functions due to its local generalization of weight updating, single structure and easy processing. In the training phase, the disadvantage of some CMAC models is unstable phenomenon...
متن کاملAn Incremental Learning Algorithm That Optimizes Network Size and Sample Size in One Trial
| A constructive learning algorithm is described that builds a feedforward neural network with an optimal number of hidden units to balance convergence and generalization. The method starts with a small training set and a small network, and expands the training set incrementally after training. If the training does not converge, the network grows incrementally to increase its learning capacity....
متن کامل