Difference between revisions of "Deliverable 5.1"
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Over the project, so far, we have identified a good deal of relevant machine learning literature. Much is detailed in terms of references to works on [http://mageec.org/wiki/Literature the literature wiki page]. A number of practical techniques have been identified and what remains is to write this up into a review in the form of a technical note. | Over the project, so far, we have identified a good deal of relevant machine learning literature. Much is detailed in terms of references to works on [http://mageec.org/wiki/Literature the literature wiki page]. A number of practical techniques have been identified and what remains is to write this up into a review in the form of a technical note. | ||
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| + | Some of the research involved has highlighted changes in directions from inputs by others or research itself. There has been valuable insight into the ML techniques used by MILEPOST by contact directly to the authors who conversed on the subject. This kind of literature has helped refine the direction for the ML, when a previous aim to reduce feature space was attempted used PCA but later rescinded by the communication received back from the authors of MILEPOST. A new direction ensued which allowed us to work towards specific algorithms that were used by them, enabling a good measure to compare against later on. | ||
Latest revision as of 13:56, 2 September 2013
Deliverable 5.1: Machine Learning Literature Review
Status: Ongoing
Over the project, so far, we have identified a good deal of relevant machine learning literature. Much is detailed in terms of references to works on the literature wiki page. A number of practical techniques have been identified and what remains is to write this up into a review in the form of a technical note.
Some of the research involved has highlighted changes in directions from inputs by others or research itself. There has been valuable insight into the ML techniques used by MILEPOST by contact directly to the authors who conversed on the subject. This kind of literature has helped refine the direction for the ML, when a previous aim to reduce feature space was attempted used PCA but later rescinded by the communication received back from the authors of MILEPOST. A new direction ensued which allowed us to work towards specific algorithms that were used by them, enabling a good measure to compare against later on.
