Welcome to JENT its Friday 19th of January 2018

Journal of Environmental Nanotechnology

(A Quarterly Peer-reviewed and Refereed International Journal)
ISSN(Print):2279-07 48; ISSN(Online):2319-5541
CODEN:JENOE2

An Overview of Statistical Pattern Recognition - A Review

Abstract

Pattern recognition is a branch of machine learning that deals with the recognition of patterns and regularities in data, in some cases considered to be nearly synonymous with machine learning.Pattern recognition systems are in many cases trained from labeled as a training data supervised learning but when no labeled data are accessible. In other algorithms can be used to discover previously unknown patterns unsupervised learning. The term pattern recognition is used in data mining and knowledge discovery in databases(KDD) are hard to separate they are largely overlap in their scope. Machine learning is the common term for supervised learning methods and ordinates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection for using business. Pattern recognition has its origins in engineering, and the term is popular in the context for computer vision conference is named Conference on Computer Vision and Pattern Recognition. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern and machine learning are focuses on maximizing the recognition rates. These domains have evolved substantially from their roots in artificial intelligence engineering and statistics and they become increasingly similar by accommodate development and ideas from each other. In machine learning, pattern recognition is the assignment of a label to a given input value. Discriminate analysis was introduced for this same purpose in statistics. Pattern recognition is classification, which attempts to assign each input value to one of a given set of classes. However, pattern recognition is a more general problem that envelope other types of output as well. In sequence labeling it assigns a class to each member of a sequence of values and parsing that assigns a parse tree to an input sentence they describing the syntactic structure of the sentence.

Article Type: Review Article

Corresponding Author: G. Arunasenbagam 2  

Email: Arunagopal2208@gmail.com

This article has not yet been cited.

C. Kalaiselvi 1,  G. Arunasenbagam 2*.  

1. Department of Computer Applications, Tirupur Kumaran College for Women, Tirupur,TN, India.

2. Department of Computer Science, Tirupur Kumaran College for Women, Tirupur,TN, India.

J. Environ. Nanotechnol., Volume 6, No. 1 pp. 75-78
ISSN: 2279-0748 eISSN: 2319-5541
ENT171235.pdf
Download Citation

Reference

Baldi, P. F. and Hornik, K., Learning in linear neural networks: A survey, {IEEE}Trans. Neural Netw., 6(4), 837-858(2002). doi: 10.1109/72.392248 Fu, K. S. and Booth, T. L., Grammatical inference: Introduction and survey: Part I, {IEEE}Trans. Pattern Anal. Mach. Intell., 8(3), 343-359(1986). doi:10.1109/TPAMI.1986.4767796 Fu, K. S., Learning control systems: review and outlook, {IEEE}Trans. Pattern Anal. Mach. Intell., 8(3), 327-342(1986). doi:ieeecomputersociety.org/10.1109/TPAMI.1986.4767795 Gelfand, S. B. and Delp, E. J., On Tree Structured Classifiers, Artificial Neural Networks and Statistical Pattern Recognition, 1(1), 51 – 70(1991). Gelfand, S. B., Ravishankar, C. S. and Delp, E. J., An iterative growing and pruning algorithm for classification tree design, {IEEE}Trans. Pattern Anal. Mach. Intell., 13(2), 163-174(1991). doi:10.1109/34.67645 Jain, A. K. and Chandrasekaran, B., Dimensionality and sample size considerations in pattern recognition practice, Handbook of Statistics, 2(39), 835-855(1987). doi: 10.1016/s0169-7161(82)02042-2 Jain, A. K., Mao, J. and Mohiuddin, K. M., Artificial neural networks: A tutorial, Computer, 29(3), 31-44(1996). doi: 10.1109/2.485891 Quinlan, J. R., Simplifying decision trees, Int. J. Man - Machine Studies, 27(3), 221-234(1987). doi:10.1016/s0020-7373(87)80053-6 Raudys, S. J and Jain, A. K., Small sample size effects in statistical pattern recognition: Recommendations for practitioners, {IEEE}Trans. Pattern Anal. Mach. Intell, 13(3), 252-264(1991). doi:10.1109/34.75512 Reed, R., Pruning algorithms - A survey, {IEEE} Trans. Neural Netw., 4(5), 740-747(1993). doi: 10.1109/72.248452 Tsoi, A. C. and Back, A. D., Locally recurrent globally feed forward networks - A critical review of architectures, {IEEE}Trans. Neural Netw., 5(2), 229-239(1994). doi: 10.1109/72.279187 Wood, J., Invariant pattern recognition: A review, Pattern Recog., 29(1), 01-17(1996). doi:10.1016/0031-3203(95)00069-0 Yu G. Smetanin, Neural networks as systems for pattern recognition: A review, Pattern Recognition and Image Analysis, 5(2), 254-293(1995).
>>