Tree ensembles with rule structured horseshoe regularization

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Tree ensembles for predicting structured outputs

In this paper, we address the task of learning models for predicting structured outputs. We consider both global and local predictions of structured outputs, the former based on a single model that predicts the entire output structure and the latter based on a collection of models, each predicting a component of the output structure. We use ensemble methods and apply them in the context of pred...

متن کامل

THRUST Evolutionary and Tree - Based Rule Ensembles

Ensemble rule based classification methods have been popular for a while in the machine-learning literature (Hand, 1997). Given the advent of low-cost, high-computing power, we are curious to see how far can we go by repeating some basic learning process, obtaining a variety of possible inferences, and finally basing the global classification decision on some sort of ensemble summary. Some gene...

متن کامل

Rule and Tree Ensembles for Unrestricted Coreference Resolution

In this paper, we describe a machine learning system based on rule and tree ensembles for unrestricted coreference resolution. We use Entropy Guided Transformation Learning (ETL) and Decision Trees as the base learners, and, respectively, ETL Committee and Random Forest as ensemble algorithms. Our system is evaluated on the closed track of the CoNLL 2011 shared task: Modeling Unrestricted Coref...

متن کامل

Interpreting Tree Ensembles with inTrees

Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also b...

متن کامل

Diversity regularization in deep ensembles

Calibrating the confidence of supervised learning models is important for a variety of contexts where the certainty over predictions should be reliable. However, it as been reported that deep neural network models are often too poorly calibrated for achieving complex tasks requiring reliable uncertainty estimates in their prediction. In this work, we are proposing a strategy for training deep e...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: The Annals of Applied Statistics

سال: 2018

ISSN: 1932-6157

DOI: 10.1214/18-aoas1157