An Extended TopoART Network for the Stable On-line Learning of Regression Functions
نویسنده
چکیده
In this paper, a novel on-line regression method is presented. Due to its origins in Adaptive Resonance Theory neural networks, this method is particularly well-suited to problems requiring stable incremental learning. Its performance on five publicly available datasets is shown to be at least comparable to two established off-line methods. Furthermore, it exhibits considerable improvements in comparison to its closest supervised relative Fuzzy ARTMAP.
منابع مشابه
Episodic Clustering of Data Streams Using a Topology-Learning Neural Network
In this paper, an extension of the unsupervised topologylearning TopoART neural network is presented. Like TopoART, it is capable of stable incremental on-line clustering of real-valued data. However, it incorporates temporal information in such a way that consecutive input vectors with a low distance in the input space are summarised to episode-like clusters. Inspired by natural memory systems...
متن کاملIncremental On-line Clustering with a Topology-Learning Hierarchical ART Neural Network Using Hyperspherical Categories
Incremental on-line learning is an important branch of machine learning. One class of approaches particularly well-suited to such tasks are Adaptive Resonance Theory (ART) neural networks. This paper presents a novel ART network combining the ability of noise-insensitive, incremental clustering and topology learning at different levels of detail from TopoART with Euclidean similarity measures a...
متن کاملTopoART: A Topology Learning Hierarchical ART Network
In this paper, a novel unsupervised neural network combining elements from Adaptive Resonance Theory and topology learning neural networks, in particular the Self-Organising Incremental Neural Network, is introduced. It enables stable on-line clustering of stationary and non-stationary input data. In addition, two representations reflecting different levels of detail are learnt simultaneously. ...
متن کاملDesigning stable neural identifier based on Lyapunov method
The stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. This paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (MDNN) and studies the stability of this algorithm. Also, stable learning algorithm for parameters of ...
متن کاملAn Incremental On-line Classifier for Imbalanced, Incomplete, and Noisy Data
Incremental on-line learning is a research topic gaining increasing interest in the machine learning community. Such learning methods are highly adaptive, not restricted to distinct training and application phases, and applicable to large volumes of data. In this paper, we present a novel classifier based on the unsupervised topologylearning TopoART neural network. We demonstrate that this clas...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011