Evaluation of Robustness in a Learning Algorithm that Minimizes Output Variation for Handprinted Kanji Pattern Recognition (Special Issue on Neurocomputing)
スポンサーリンク
概要
- 論文の詳細を見る
This paper uses both network analysis and experiments to confirm that the neural network learning algorithm that minimizes output variation (BPV) provides much more robustness than back-propagation (BP) or BP with noise-modified training samples (BPN). Network analysis clarifies the relationship between sample displacement and what and how the network learns. Sample displacement generates variation in the output of the output units in the output layer. The output variation model introduces two types of deformation error, both of which modify the mean square error. We propose a new error which combines the two types of deformation error. The network analysis using this new error considers that BPV learns two types of training samples where the modification is either towards or away from the category mean, which is defined as the center of sample distribution. The magnitude of modification depends on the position of the training sample in the sample distribution and the degree of learning completion. The conclusion is that BPV learns samples modified towards to the category mean more stronger than those modified away from the category mean, namely it achieves nonuniform learning. Another conclusion is that BPN learns from uniformly modified samples. The conjecture that BPV is much more robust than the other two algorithms is made. Experiments that evaluate robustness are performed from two kinds of viewpoints: overall robustness and specific robustness. Benchmark studies using distorted handprinted Kanji character patterns examine overall robustness and two specifically modified samples (noise-modified samples and directionally-modified samples) examine specific robustness. Both sets of studies confirm the superiority of BPV and the accuracy of the conjecture.
- 社団法人電子情報通信学会の論文
- 1994-04-25
著者
関連論文
- Single-Board SIMD Processors Using Gate-Array LSIs for Parallel Processing (Special Issue on ASICs for Automotive Electronics)
- Evaluation of Robustness in a Learning Algorithm that Minimizes Output Variation for Handprinted Kanji Pattern Recognition (Special Issue on Neurocomputing)