Adnan Masood, a Machine Learning Phd's musings on Life, Technology, Research & Development
As a follow up on MIT's tackling the challenges of Big Data, I am currently in Boston attending Machine Learning for Big Data and Text Processing Classification (and therefore blogging about it for posterity based on public domain data / papers - nothing posted here is MIT proprietary info to violate any T&C). MIT professional education courses are…
Compiled the list of reviews and published on my academic site. Probabilistic Interestingness Measures, Probabilistic Graphical Models and Outlier Analysis / Bibtex and a poster, just cuz.
Causality, Probability and Time by Dr. Samantha Kelinberg is a whirlwind yet original journey of the interdisciplinary study of probabilistic temporal logic and causal inference. Probabilistic causation is a fairly demanding area of study which studies the relationship between cause and effect using the tools of probability theory. Judea Pearl, in his seminal text "Causality:…
A #NoteToSelf style post regarding collection of bayesian network repositories including but not limited to bnet, net, bif, dsc and rda files. GeNIe and SMILE Network Repository http://genie.sis.pitt.edu/networks.html BNLearn http://www.bnlearn.com/bnrepository/ University of Hebrew Bayesian Network Repository http://www.cs.huji.ac.il/~galel/Repository/ DSL lab Network Repository http://genie.sis.pitt.edu/networks.html Aalborg University Repository http://www.cs.auc.dk/research/DSS/Misc/networks.html Norsys Bayes Net Library http://www.norsys.com/networklibrary.html Encog Project - Example…
As statistician Dennis Lindley famously said, "Inside every nonBayesian there is a Bayesian struggling to get out"; it would be safe to interpolate that Sharon McGrayne's interesting tale of trials and triumph of the Bayes Rule, or more accurately Bayes-Laplace-Price rule, is an excellent historical journey, which may help get your Bayesian out of the…
If you are scouring for an exploratory text in probabilistic reasoning, basic graph concepts, belief networks, graphical models, statistics for machine learning, learning inference, naïve Bayes, Markov models and machine learning concepts, look no further. Dr. Barber has done a praiseworthy job in describing key concepts in probabilistic modeling and probabilistic aspects of machine learning.…
S. Abe and T. Inoue. Fuzzy support vector machines for multiclass problems.In ESANN 2002 Proceedings, pages 113-118, 2002. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD Conference, 1993. A. Alink, C. M. Schwiedrzik, A. Kohler, W. Singer, and L.…
The idea of using of Bayesian Belief Networks in digital forensics to quantify the evidence has been around for a while now. To provide qualitative approaches to Bayesian evidential reasoning in the digital Meta-Forensics is however relatively new in the decision support systems research. For law enforcement, decision support and application of data mining techniques to “soft” forensic evidence is…
I have recently came across Wainwright & Jordan's paper on exponential families, graphical models, and variational inference and found it to be quite comprehensive and unifying introduction of the topic. Probabilistic graphical models use a graph-based representation as the basis for compactly encoding a complex distribution over a high-dimensional space. If you are familiar with Koller and Friedman's work on…
In Bayesian Artificial Intelligence, authors Kevin B. Korb and Ann E. Nicholson points out the non-Bayesian nature of Belief networks. The researchers note Many AI researchers like to point out that Bayesian networks are not inherently Bayesian at all; some have even claimed that the label is a misnomer. At the 2002 Australasian Data Mining Workshop, for example, Geoff…