Amazon Technologies, Inc.

Daniel Blick

Jin Li

Leo Parker Dirac

Sriram Krishnan

Aleksandr Mikhaylovich Ingerman

Saman Zarandioon

Nicolle M Correa

Robert Matthias Steele

Donghui Zhuo

Charles Eric Dannaker

Tianming Zheng

Sudhakar Rao Puvvadi

Tarun Agarwal

Rakesh Ramakrishnan

Jun Qian

Polly Po Lee

Ralf Herbrich

Michael Brueckner

Nina Mishra

Sudipto Guha

Okke Joost Schrijvers

Polly Po Yee Lee

Random cut trees are generated with respective to respective samples of a baseline set of data records of a data set for which outlier detection is to be performed. To construct a particular random cut tree, an iterative splitting technique is used, in which the attribute along which a given set of data records is split is selected based on its value range. With respect to a newly-received data record of the stream, an outlier score is determined based at least partly on a potential insertion location of a node representing the data record in a particular random cut tree, without necessarily modifying the random cut tree.

Outlier detection for streaming data

An indication of a data source to be used to train a linear prediction model is obtained. The model is to generate predictions using respective parameters assigned to a plurality of features derived from observation records of the data source. The parameter values are stored in a parameter vector. During a particular learning iteration of the training phase of the model, one or more features for which parameters are to be added to the parameter vector are identified. In response to a triggering condition, parameters for one or more features are removed from the parameter vector based on an analysis of relative contributions of the features represented in the parameter vector to the model's predictions. After the parameters are removed, at least one parameter is added to the parameter vector.

Optimized training of linear machine learning models

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Mountain View, CA, United States of America

Daniel Blick