Recognise the Degraded Devnagari Script by Dimensionality Reduction Linear and quadratic Classifiers using Fisher Linear Discriminant
DOI:
https://doi.org/10.26438/ijcse/v6i11.336340Keywords:
Linear, Quadratic, Fisher Linear Discriminant, Cross validation, Feature Extraction, DimensionalityAbstract
In this paper we are implementing parametric classifier Linear and quadratics using fisher linear discriminant for find the misclassification rate using cross validation, useful in recognizing the degraded devnagari script scan document.Dimensionality reduction is the process of transforming input data into a lower dimensional space where a more efficient classifier can be built are divided in two groups: Feature extraction, which map input data using linear transformation i.e. a transformation matrix and feature selection, which performs the mapping by selecting a subset of the original features.Feature extraction methods are supported by fisher’s linear discriminant function.Feature selection is use to choose an optimal subset according to some criterion of cardinality m among the d input features. In feature ranking each Feature is evaluated individually according to the chosen criterion, and the values are then sorted the m features with the best value of the criterion are retained for classification. Also we focus on learning machine stages which consists of two stages: dimensionality reduction and classification.
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