Model-building under the supervised learning domain potentially face a dual learning problem of identifying both the parameters of the model and the subset of (domain) attributes necessary to support the model: or an embedded as opposed to wrapper or filter based design. Genetic Programming (GP) has always addressed this dual problem, however, further implicit assumptions are made which potenti...

Propose a novel approach of rule extracting based on attribute significance and decision classification (REBSC). Condition attributes are only discretized with their own features in local discretization theory, and the eventual rule set normally can be achieved after attribute reduction. The REBSC approach given in this paper makes the most of each condition attribute significance and classific...

In this work we present a point classification algorithm for multi-variate data. Our method is based on the concept of attribute subspaces, which are derived from a set of user specified attribute target values. Our classification approach enables users to visually distinguish regions of saliency through concurrent viewing of these subspaces in single images. We also allow a user to threshold t...

Word spotting is to make searchable unindexed image documents by locating word/words in a doc-ument image, given a query word. This problem is challenging, mainly due to the large numberof word classes with very small inter-class and substantial intra-class distances. In this paper, asegmentation-based word spotting method is presented for multi-writer Persian handwritten doc-...

spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

Data classification is a process which groups objects with common properties into classes and produces a classification scheme over a set of data objects. Data classification is useful for understanding and organizing database data and building hierarchical schemes in databases. We investigate data classification in relational databases and develop a method for data classification by concept-ba...

Phone attributes, known also as distinctive or phonological features, belong to important classification of the speech sounds used in automatic speech processing. Training of conventional phone attribute detectors (classifiers), either based on acoustic measurements or deep learning approaches, requires decent phone boundary segmentation. This paper proposes a solution to train a phone attribut...