This is a nice course on Memory Based Reasoning in AI taught by Dr. Deepak Khemani and Dr. Sutanu Chakraborti at the Department of Computer Science & Engineering, Indian Institute of Technology Madras, Chennai, India. See
Happy to see that the following readings from the course are based on two articles we published long time ago at Mi Islita.com, now a search engine site about Puerto Rico resources.
We have expanded the number of similarity measures that our Binary Similarity Calculator computes from 30 to 72 (and counting…)
Same measures with different names have been consolidated into a single record, and different measures with same name have been enumerated as necessary.
These similarity coefficients have many applications across disciplines: from bioinformatics to chemistry, chemometrics, statistics, data mining, information retrieval, marketing research, etc.
We have also included the new similarity measures proposed by Consonni & Todeschini (2012), and Todeschini, et al (2012).
Our Tutorial on Distance and Similarity was also updated, accordingly. Check it out at
References
Consonni, V. and Todeschini, R. (2012). New Similarity Coefficients for Binary Data. MATCH Commun. Math. Comput. Chem. 68, 581-592.
Todeschini, R., Consonni, V., Xiang, H., Holliday, J., Buscema, M., and Willet, P. (2012). Similarity Coefficients for Binary Chemoinformatics Data: Overview and Extended Comparison Using Simulated and Real Data Sets. J. Chem. Inf. Model. 52 (11).
The calculations presented in the new tutorial are so simple that can be carried out with a spreadsheet, online calculator, or by hand. Thus, the article is suitable for those interested in learning about vector space models, but that lack of a linear algebra background.
Another fast-track tutorial updated and improved is back!
This is a fast track tutorial on vector space calculations. A linear algebra approach is used. The tutorial covers term-document and term-query matrices, matrix transposition, dot products, cosine similarities, and local and global weights.
