THE GENERALIZATION OF A CONSTRUCTIVE ALGORITHM IN PATTERN CLASSIFICATION PROBLEMS

The use of a constructive algorithm for pattern classification is examined. The algorithm, a ‘Perceptron Cascade’, has been shown to converge to zero errors whilst learning any consistent classification of real-valued pattern vectors (Burgess, 1992). Limiting network size and producing bounded decision regions are noted to be important for the generalization ability of a network. A scheme is suggested by which a result on generalization (Vapnik, 1992) may enable calculation of the optimal network size. A fast algorithm for principal component analysis (Sirat, 1991) is used to construct ‘hyper-boxes’ around each class of patterns to ensure bounded decision regions. Performance is compared with the Gaussian Maximum Likelihood procedure in three artificial problems simulating real pattern classification applications.

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Classification of reduction invariants with improved backpropagation

International Journal of Mathematics and Mathematical Sciences ◽

10.1155/s0161171202006117 ◽

2002 ◽

Vol 30 (4) ◽

pp. 239-247

Author(s):

S. M. Shamsuddin ◽

M. Darus ◽

M. N. Sulaiman

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Feature Space ◽

Original Data ◽

Component Analysis ◽

Classification Problems ◽

Share Prices ◽

Data Space ◽

Backpropagation Network

Data reduction is a process of feature extraction that transforms the data space into a feature space of much lower dimension compared to the original data space, yet it retains most of the intrinsic information content of the data. This can be done by using a number of methods, such as principal component analysis (PCA), factor analysis, and feature clustering. Principal components are extracted from a collection of multivariate cases as a way of accounting for as much of the variation in that collection as possible by means of as few variables as possible. On the other hand, backpropagation network has been used extensively in classification problems such as XOR problems, share prices prediction, and pattern recognition. This paper proposes an improved error signal of backpropagation network for classification of the reduction invariants using principal component analysis, for extracting the bulk of the useful information present in moment invariants of handwritten digits, leaving the redundant information behind. Higher order centralised scale- invariants are used to extract features of handwritten digits before PCA, and the reduction invariants are sent to the improved backpropagation model for classification purposes.

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Pattern Classification of Acoustic Emission Signals during Wood Drying by Principal Component Analysis and Artificial Neural Network

Key Engineering Materials - Advances in Fracture and Strength ◽

10.4028/0-87849-978-4.1962 ◽

2005 ◽

pp. 1962-1967

Author(s):

Ki Bok Kim ◽

Ho Yang Kang ◽

Dong Jin Yoon ◽

Man Yong Choi

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Artificial Neural Network ◽

Acoustic Emission ◽

Pattern Classification ◽

Principal Component ◽

Component Analysis ◽

Wood Drying ◽

Artificial Neural

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Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.13 ◽

2017 ◽

Vol 7 (8) ◽

pp. 30

Author(s):

Hyeuk Kim

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Cluster Analysis ◽

Unsupervised Learning ◽

Principal Component ◽

Component Analysis ◽

Baseball Players ◽

Partitioning Around Medoids ◽

Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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