hunch_net hunch_net-2005 hunch_net-2005-59 knowledge-graph by maker-knowledge-mining

59 hunch net-2005-04-22-New Blog: [Lowerbounds,Upperbounds]


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Introduction: Maverick Woo and the Aladdin group at CMU have started a CS theory-related blog here .


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1 Maverick Woo and the Aladdin group at CMU have started a CS theory-related blog here . [sent-1, score-1.172]


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tfidf for this blog:

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[('cmu', 0.526), ('cs', 0.514), ('group', 0.408), ('blog', 0.403), ('started', 0.361)]

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Introduction: Maverick Woo and the Aladdin group at CMU have started a CS theory-related blog here .

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Introduction: Ed Snelson won the Predictive Uncertainty in Environmental Modelling Competition in the temp(erature) category using this algorithm . Some characteristics of the algorithm are: Gradient descent … on about 600 parameters … with local minima … to solve regression. This bears a strong resemblance to a neural network. The two main differences seem to be: The system has a probabilistic interpretation (which may aid design). There are (perhaps) fewer parameters than a typical neural network might have for the same problem (aiding speed).

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Introduction: For the Chicago 2005 machine learning summer school we are organizing, at least 5 international students can not come due to visa issues. There seem to be two aspects to visa issues: Inefficiency . The system rejected the student simply by being incapable of even starting to evaluate their visa in less than 1 month of time. Politics . Border controls became much tighter after the September 11 attack. Losing a big chunk of downtown of the largest city in a country will do that. What I (and the students) learned is that (1) is a much larger problem than (2). Only 1 prospective student seems to have achieved an explicit visa rejection. Fixing problem (1) should be a no-brainer, because the lag time almost surely indicates overload, and overload on border controls should worry even people concerned with (2). The obvious fixes to overload are “spend more money” and “make the system more efficient”. With respect to (2), (which is a more minor issue by the numbers) it i

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Introduction: Pat (the practitioner) I need to do multiclass classification and I only have a decision tree. Theo (the thoeretician) Use an error correcting output code . Pat Oh, that’s cool. But the created binary problems seem unintuitive. I’m not sure the decision tree can solve them. Theo Oh? Is your problem a decision list? Pat No, I don’t think so. Theo Hmm. Are the classes well separated by axis aligned splits? Pat Err, maybe. I’m not sure. Theo Well, if they are, under the IID assumption I can tell you how many samples you need. Pat IID? The data is definitely not IID. Theo Oh dear. Pat Can we get back to the choice of ECOC? I suspect we need to build it dynamically in response to which subsets of the labels are empirically separable from each other. Theo Ok. What do you know about your problem? Pat Not much. My friend just gave me the dataset. Theo Then, no one can help you. Pat (What a fuzzy thinker. Theo keeps jumping t

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