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    • Learning Question Classifiers Association for Computational

      classification of the sought after answer and may even suggest competition (Voorhees, 2000), participants are re quested to .. Dij = Errij * 2/(Ni + Nj),. (3).

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    • A method of combining multiple probabilistic classifiers CiteSeerX

      Keywords: Combination of multiple classifiers; Soft competition; Different feature .. where the kth feature set is used to train NI different classifiers (NI51 and ).

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    • UMICH SI650 Question Classification Kaggle

      Once the competition ends, you will see the final accuracy computed based on 100% of the test data. The evaluation metric is the accuracy of your classifier so

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    • An Unsupervised Learning Classifier with Competitive Error arXiv

      classification error probability competitive with that of popular supervised learning Keywords: Unsupervised Learning, Linear Classifiers, Neural Models. 1.

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      classifiers for gold ore competitive_Tin Ore,Chrome Lumpy and Chrome Ore Classifier for Iron ore Hot sale Copper/Zinc/Nickel/Lead/Mo/gold ore process

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    • Predicting mTOR Inhibitors with a Classifier Using Recursive

      May 12, 2014 Recently, many potential ATP competitive inhibitors of mTOR have been . the prediction accuracy for non inhibitors (Qni), overall predictive

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    • Optimal Probability Estimation with Applications to Cornell ECE

      uniformly optimal combined probability estimator, construct a classifier that uniformly approaches the error .. Our objective is to derive a competitive classifier whose error is close to the best possible .. Let p be a distribution with ni def. = π.

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    • Leaf Classification Playground Competition: Winning Kernels No

      Mar 15, 2017 The Leaf Classification playground competition ran on Kaggle from August 2016 to February 2017. Over 1,500 Kagglers competed to

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    • Towards Competitive Classifiers for Unbalanced Classification

      Aug 31, 2016 in proposing competitive solutions to the class imbalance problem, which are capable of determining whether a classifier is competitive.

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    • Competitive generative models with structure learning for NLP

      Competitive generative models with structure learning for NLP classification tasks. Kristina Toutanova. Microsoft Research. Redmond, WA kristout@microsoft.

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