This Blog Provide Topics, Abstracts, Documentations, Slides for various Seminars, Projects, Paper Presentations. After Reading Abstract You Can Download Corresponding Paper By Clicking The Link Given At The Bottom. On The Right Side Bar Select Your Branches CSE, ECE, EEE, IT, MCA, MBA, Civil, Mechanical Departments And More Stuff Will Be Added From Time To Time. So Please Be In Touch With This Blog For More And Apt Information.
|Speech Compression| |Data Security| |Artificial Neural Networks| |Moletronics| |AI Speech Recognition| |ATM| |Blue Eyes| |Brain Computer Interface| |Fuzzy Logic| |Mobile Voting| |Information Security Using Steganography| |Modern Irrigation Systems| |Asynchronous Chip| |Smartphone| |Gizmag|Subtractive Synthesis | Spread Spectrum | Speech Compression | Paper Batteries | Satellite Encryption | Robotics 1 2 | Silicon in Nanotechnology | Renewable Energy Systems | Reed Solomon Code | Vlsi Paper Presentation | Green Nanotechnology | Aerospace Nanotechnology | Nanotechnology | Brain Controlled Car 1 | Bubble Power | Brain Machine Interface | Beam Robotics Nervous Systems | Artificial Photosynthesis | Neural Networks | Adaptive Filtering | Finger Print Recognizer | Vlsi Chip | Digital Water Marking |
Incremental Learning of Chunk Data for Online Pattern Classification Systems
This paper presents a pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where training samples are presented only once. For this purpose, we have extended incremental principal component analysis (IPCA) and some classifier models were effectively combined with it. However, there was a drawback in this approach that training samples must be learned one by one due to the limitation of IPCA. To overcome this problem, we propose another extension of IPCA called chunk IPCA in which a chunk of training samples is processed at a time. In the experiments, we evaluate the classification performance for several large-scale data sets to discuss the scalability of chunk IPCA under one-pass incremental learning environments. The experimental results suggest that chunk IPCA can reduce the training time effectively as compared with IPCA unless the number of input attributes is too large. We study the influence of the size of initial training data and the size of given chunk data on classification accuracy and learning time. We also show that chunk IPCA can obtain major eigenvectors with fairly good approximation