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  • 1
    ISSN: 1432-1432
    Keywords: Key words: Maximum likelihood — General reversible Markov model — Amino acid substitution — Chloroplast DNA — AA LogDet
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: Abstract. Maximum likelihood (ML) phylogenies based on 9,957 amino acid (AA) sites of 45 proteins encoded in the plastid genomes of Cyanophora, a diatom, a rhodophyte (red algae), a euglenophyte, and five land plants are compared with respect to several properties of the data, including between-site rate variation and aberrant amino acid composition in individual species. Neighbor-joining trees from AA LogDet distances and ML analyses are seen to be congruent when site rate variability was taken into account. Four feasible trees are identified in these analyses, one of which is preferred, and one of which is almost excluded by statistical criteria. A transition probability matrix for the general reversible Markov model of amino acid substitutions is estimated from the data, assuming each of these four trees. In all cases, the tree with diatom and rhodophyte as sister taxa was clearly favored. The new transition matrix based on the best tree, called cpREV, takes into account distinct substitution patterns in plastid-encoded proteins and should be useful in future ML inferences using such data. A second rate matrix, called cpREV*, based on a weighted sum of rate matrices from different trees, is also considered.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    International journal on digital libraries 3 (2000), S. 140-151 
    ISSN: 1432-1300
    Keywords: Key words: Fuzzy retrieval – Query term expansion – Retrieval speed – Confusion term – OCR
    Source: Springer Online Journal Archives 1860-2000
    Topics: Information Science and Librarianship
    Notes: Abstract. Optical character reader (OCR) misrecognition is a serious problem when OCR-recognized text is used for retrieval purposes in digital libraries. We have proposed fuzzy retrieval methods that, instead of correcting the errors manually, assume that errors remain in the recognized text. Costs are thereby reduced. The proposed methods generate multiple search terms for each input query term by referring to confusion matrices, which store all characters likely to be misrecognized and the respective probability of each misrecognition. The proposed methods can improve recall rates without decreasing precision rates. However, a few million search terms are occasionally generated in English-text fuzzy retrieval, giving an intolerable effect on retrieval speed. Therefore, this paper presents two remedies to reduce the number of generated search terms while maintaining retrieval effectiveness. One remedy is to restrict the number of errors included in each expanded search term, while the other is to introduce another validity value different to our conventional one. Experimental results indicate that the former remedy reduced the number of terms to about 50 and the latter to not more than 20.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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