By Suraiya Jabin
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In the results mentioned in Section 7 we could have attained better precision in some cases if better algorithm implementations were available (or any bugs in exiting ones fixed). 48 Table 10. 2 Future work The general goals of the future and ongoing research include: • There are a lot more algorithms to implement and test for the existing tasks. • Apply to more case studies. ). 35 MARF: Comparative Algorithm Studies for Better Machine Learning • Scalability studies with the General Intensional Programming System (GIPSY) project (see Mokhov & Paquet (2010); Paquet (2009); Paquet & Wu (2005); The GIPSY Research and Development Group (2002–2010); Vassev & Paquet (2008)).
Top Most Accurate Configurations for Speaker Identification, 1st and 2nd Guesses, Median Clustering (Mokhov (2008d)) 2. -fft, -lpc, and -aggr correspond to the FFT-based, LPC-based, or aggregation of the two feature extractors; -cos, -eucl, -cheb, -hamming, -mink, and –diff correspond to the classifiers, such as cosine similarity measure, Euclidean, Chebyshev, Hamming, Minkowski, and diff distances respectively. In Mokhov & Debbabi (2008), an experiment was conducted to use a MARF-based FileTypeIdentApp for bulk forensic analysis of file types using signal processing techniques as opposed to the Unix file utility (see Darwin et al.
ISBN 1-56592262-X. , Sylva, L. D. & DEFT (2010). php. , Sierra, K. & Bates, B. (2004). Head First Design Patterns, first edn, O’Reilly. html. , Johnson, R. & Vlissides, J. (1995). Design Patterns: Elements of Reusable Object-Oriented Software, Addison-Wesley. ISBN: 0201633612. Garcia, E. (2006). Cosine similarity and term weight tutorial, [online]. html. Green, D. (2001–2005). Java reflection API, Sun Microsystems, Inc. html. Hamming, R. W. (1950). Error detecting and error correcting codes, Bell System Technical Journal 26(2): 147–160.
Robot Learning by Suraiya Jabin