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Some topics in dimension reduction and clusteringZhao, Jianhua, January 2009 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 144-150). Also available in print.
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Meta-learning strategies, implementations, and evaluations for algorithm selection /Köpf, Christian Rudolf. January 1900 (has links)
Thesis (doctorate)--Universität Ulm, 2005. / Includes bibliographical references (p. 227-248).
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Matrix nearness problems in data miningSra, Suvrit, January 1900 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.
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Data mining logic explanations from numerical data /Riehl, Katrina. January 2006 (has links)
Thesis. / Includes vita. Includes bibliographical references (leaves 79-86)
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Investigating machine learning methods in chemistryLowe, Robert Alexander January 2012 (has links)
No description available.
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Mining statistical correlations with applications to software analysisDavis, Jason Victor. January 1900 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.
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SAWTOOTH learning from huge amounts of data /Orrego, Andrés Sebastián. January 1900 (has links)
Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xi, 143 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 138-143).
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Optimization in logical analysis of dataBonates, Tiberius. January 2007 (has links)
Thesis (Ph. D.)--Rutgers University, 2007. / "Graduate Program in Operations Research." Includes bibliographical references (p. 95-103).
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Enhanced root extraction and document classification algorithm for Arabic textAlsaad, Amal January 2016 (has links)
Many text extraction and classification systems have been developed for English and other international languages; most of the languages are based on Roman letters. However, Arabic language is one of the difficult languages which have special rules and morphology. Not many systems have been developed for Arabic text categorization. Arabic language is one of the Semitic languages with morphology that is more complicated than English. Due to its complex morphology, there is a need for pre-processing routines to extract the roots of the words then classify them according to the group of acts or meaning. In this thesis, a system has been developed and tested for text classification. The system is based on two stages, the first is to extract the roots from text and the second is to classify the text according to predefined categories. The linguistic root extraction stage is composed of two main phases. The first phase is to handle removal of affixes including prefixes, suffixes and infixes. Prefixes and suffixes are removed depending on the length of the word, while checking its morphological pattern after each deduction to remove infixes. In the second phase, the root extraction algorithm is formulated to handle weak, defined, eliminated-long-vowel and two-letter geminated words, as there is a substantial great amount of irregular Arabic words in texts. Once the roots are extracted, they are checked against a predefined list of 3800 triliteral and 900 quad literal roots. Series of experiments has been conducted to improve and test the performance of the proposed algorithm. The obtained results revealed that the developed algorithm has better accuracy than the existing stemming algorithm. The second stage is the document classification stage. In this stage two non-parametric classifiers are tested, namely Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The system is trained on 6 categories: culture, economy, international, local, religion and sports. The system is trained on 80% of the available data. From each category, the 10 top frequent terms are selected as features. Testing the classification algorithms has been done on the remaining 20% of the documents. The results of ANN and SVM are compared to the standard method used for text classification, the terms frequency-based method. Results show that ANN and SVM have better accuracy (80-90%) compared to the standard method (60-70%). The proposed method proves the ability to categorize the Arabic text documents into the appropriate categories with a high precision rate.
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Computer-aided underground mining machine sequencingIlango, Sankaralingam January 1987 (has links)
No description available.
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