Abstract
In this paper, an incremental learning model called Resource Allocating Network with Long-Term Memory (RAN-LTM) is extended such that the learning is conducted with some autonomy for the following functions: 1) data collection for initial learning, 2) data normalization, 3) addition of radial basis functions (RBFs), and 4) determination of RBF cen-ters and widths. The proposed learning algorithm called Autonomous Learning algorithm for Resource Allocating Network (AL-RAN) is divided into the two learning phases: initial learning phase and incremental learning phase. And the former is further divided into the autonomous data collection and the initial network learning. In the initial learning phase, training data are first collected until the class separability is converged or has a significant dif-ference between normalized and unnormalized data. Then, an initial structure of AL-RAN is autonomously determined by selecting a moderate number of RBF centers from the collected data and by defining as large RBF widths as possible within a proper range. After the initial learning, the incremental learning of AL-RAN is conducted in a sequential way whenever a new training data is given. In the experiments, we evaluate AL-RAN using five benchmark data sets. From the experimental results, we confirm that the above autonomous functions work well and the efficiency in terms of network structure and learning time is improved without sacrificing the recognition accuracy as compared with the previous version of AL-RAN.
Highlights
When a learning model is applied to real-world problems, it does not always work well unless a human supervisor participates initial settings such as choosing proper parameters and selecting the data preprocessing depending on given problems
An incremental learning model called Resource Allocating Network with Long-Term Memory (RAN-LTM) is extended such that the learning is conducted with some autonomy for the following functions: 1) data collection for initial learning, 2) data normalization, 3) addition of radial basis functions (RBFs), and 4) determination of RBF centers and widths
Since a two-stage clustering is applied to selecting RBF centers and widths in AL-RAN(new), a smaller number of RBFs are selected after the initial learning phase
Summary
When a learning model is applied to real-world problems, it does not always work well unless a human supervisor participates initial settings such as choosing proper parameters and selecting the data preprocessing (e.g., feature extraction / selection and data normalization) depending on given problems. AL-RAN is a one-pass incremental learning model which consists of the following autonomous functions: 1) data collection for initial learning, 2) data normalization, 3) addition of radial basis functions (RBFs), and 4) determination of RBF centers and widths. In order to determine the initial structure of AL-RAN, a two-stage clustering algorithm is performed for the collected data to obtain a moderate number of prototypes and their cluster radii These prototypes and radii are set to RBF centers and widths in AL-RAN, respectively.
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