A New Schema and Landscape for Programs

Dr Kaur and I sent a paper on a new schema and landscape for IEEE TEC peer review two weeks ago.

In the paper we showcase the new schema and landscape analyses by applying it to the Santa Fe ant problem. This caused us to discover for the first time the relationship between program structures and program fitness. Traditionally the Santa Fe ant problem is well known for presenting a random fitness relationship when analyzed by any other method.

We also show for the first time the systematic approaches to fitness improvement that programs make during genetic programming runs, thereby showing that the process is very different from what a random search does. We test a new variation and representation method that were designed based on our findings and obtained more efficient evolutionary search.

Please send me an email if you would like a copy for personal review.

We are currently undecided on whether we should post the pre-review paper to Arxiv.org. Any advice?

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Some Data Mining amd Machine Learning Presentations

Statistical Aspects of Data Mining, David Mease


Human Computation, Luis von Anh


Winning the DARPA Grand Challenge, Sebastian Thrun


Scalability and Efficiency on Data Mining Applied to Internet Applocations, Wagner Meira


Sparse and large-scale learning with heterogeneous data, Gert Lanckriet


The first class(ification)-oriented representational formalism, Lev Goldfab


Using Statistics to Search and Annotate Pictures, Nuno Vasconcelos

{Sentence} -> {Word} {Space} {Word} {Stop} -> Hello world!

Welcome to my blog!

I am just about finishing my PhD and that gives me some time to express my opinions and ideas on the science and engineering of intelligent machines. Intelligent machines are machines (or systems of machines) that show abilities we commonly associate with intelligence; these include the capacity to learn, reason, plan and derive and use knowledge from their environment towards achieving some goal.

I am using the term machine in the wide sense, i.e. a machine does not have to be mechanical, it just has to have some machinery (defined as ” A system of related elements that operate in a definable manner”). There we go, you really can’t go far into machine intelligence without considering grammars. Grammars are the rules governing the operations of machines. Grammars say what machines can legally do, how they are defined to operate. Grammars can be viewed from the perspectives of being guides and/or restrictions on the operations of machines. Grammars are everywhere, trust me.

You should have guessed by now that I am a fan of grammars. My doctoral dissertation is on applying Grammatical Evolution to the task of data mining network intrusion data. I have to defend it some time next month, and then I am done (I hope). See you around.