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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates)
/Language (en\055US)
/Created (2013)
/Description-Abstract (A probabilistic model based on the horseshoe prior is proposed for learning dependencies in the process of identifying relevant features for prediction\056 Exact inference is intractable in this model\056 However\054 expectation propagation offers an approximate alternative\056 Because the process of estimating feature selection dependencies may suffer from over\055fitting in the model proposed\054 additional data from a multi\055task learning scenario are considered for induction\056 The same model can be used in this setting with few modifications\056 Furthermore\054 the assumptions made are less restrictive than in other multi\055task methods\072 The different tasks must share feature selection dependencies\054 but can have different relevant features and model coefficients\056 Experiments with real and synthetic data show that this model performs better than other multi\055task alternatives from the literature\056 The experiments also show that the model is able to induce suitable feature selection dependencies for the problems considered\054 only from the training data\056)
/Producer (Python PDF Library \055 http\072\057\057pybrary\056net\057pyPdf\057)
/Title (Learning Feature Selection Dependencies in Multi\055task Learning)
/Date (2013)
/EventType (Poster)
/Type (Conference Proceedings)
/firstpage (746)
/Book (Advances in Neural Information Processing Systems 26)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (C\056J\056C\056 Burges and L\056 Bottou and M\056 Welling and Z\056 Ghahramani and K\056Q\056 Weinberger)
/Author (Daniel Hern\341ndez\055Lobato\054 Jos\351 Miguel Hern\341ndez\055Lobato)
/lastpage (754)
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