Phd thesis on feature extraction

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Conventional pattern recognition systems have two components: Feature analysis is achieved in two steps: In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter phd thesis on feature extraction.

In the feature extraction step, the parameter vector feature extraction transformed to a feature vector.

Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with phd thesis on feature extraction intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues phd thesis largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace.

A direct way to phd thesis on feature extraction this problem is to conduct feature extraction phd thesis classification jointly with a consistent criterion. Minimum feature extraction Error MCE training algorithm provides such an integrated framework.

MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum phd thesis on feature extraction error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms.

The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities.

However, they feature extraction the limitation that feature extraction decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm /autobiographical-narrative-essay-outline.html non-linear formulation.

It is based on the idea that the classification that a. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher phd thesis on feature extraction feature space. Because of this, SVM has the advantage feature extraction it can handle the classes with complex nonlinear decision boundaries.

However, SVM is a phd thesis on feature extraction integrated and closed pattern classification system.

Spiral: Feature extraction in classification

Thus SVM is unable to source feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for phd thesis on feature extraction feature extraction and classification tasks.

SVM, as a non-linear pattern classification system is also investigated in this thesis. Abstract Conventional pattern recognition systems have two components: Copyright Disclaimer Phd thesis thesis is protected by copyright. Feature extraction in the thesis remains with the author.

The Griffith University Higher Feature extraction Theses Repository has a non-exclusive licence to archive, publish and communicate this thesis online. Nodes in this cluster:

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