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The language learning theories of Professor J. Cummins – Additive/subtractive bilingualism Cummins draws the distinction between additive bilingualism in which the first language continues to be developed and the first culture to be valued while the second language is added; and subtractive bilingualism in which the second language is added at the expense of the first language and culture, which diminish as a consequence.
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Of Paris & Goats: Remotely Building a Tech Team – Do they bring something new and additive to the team? There are certain things that. Bandwidth management is still not as good as Skype. Split screen isn’t a thing. It’s the little things..
How to use decision stump as weak learner in Adaboost? – I want to implement Adaboost using Decision Stump. Is it correct to make as many decision stump as our data set’s features in each iteration of Adaboost? For example, if I have a data set with 24
PDF Ranking with Boosted Decision Trees – 1 Introduction 2 Web Scale Information Retrieval Ranking in IR Algorithms for Ranking 3 MART Decision Trees Boosting Multiple additive regression trees 4 LambdaMART RankNet LambdaRank LambdaMART Algorithm 5 Using Multiple Rankers 6 References Hiko Schamoni (Universitat Heidelberg) Ranking with Boosted Decision Trees January 16, 2012 2 / 49
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Gradient Boosting for Regression Problems With Example. – Introduction to Gradient Boosting. The goal of the blog post is to equip beginners with the basics of gradient boosting regression algorithm to aid them in building their first model.. Gradient Boosting for regression builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions.
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models: A List of Available Models in train in caret. – Generalized Additive Model using LOESS (method = ‘gamLoess’) For classification and regression using package gam with tuning parameters: Span (span, numeric) degree (degree, numeric) Note: Which terms enter the model in a nonlinear manner is determined by the number of unique values for the predictor.