公开(公告)号：US09633315B2

公开(公告)日：2017-04-25

申请号：US13458545

申请日：2012-04-27

申请人： Olivier Chapelle ,  John Langford ,  Miroslav Dudik ,  Alekh Agarwal

发明人： Olivier Chapelle ,  John Langford ,  Miroslav Dudik ,  Alekh Agarwal

IPC分类号： G06N99/00 ,  G06F15/18

CPC分类号： G06N99/005 ,  G06F15/18

摘要： Method, system, and programs for distributed machine learning on a cluster including a plurality of nodes are disclosed. A machine learning process is performed in each of the plurality of nodes based on a respective subset of training data to calculate a local parameter. The training data is partitioned over the plurality of nodes. A plurality of operation nodes are determined from the plurality of nodes based on a status of the machine learning process performed in each of the plurality of nodes. The plurality of operation nodes are connected to form a network topology. An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.

公开(公告)号：US20130290223A1

公开(公告)日：2013-10-31

申请号：US13458545

申请日：2012-04-27

申请人： Olivier Chapelle ,  John Langford ,  Miroslav Dudik ,  Alekh Agarwal

发明人： Olivier Chapelle ,  John Langford ,  Miroslav Dudik ,  Alekh Agarwal

IPC分类号： G06F15/18

CPC分类号： G06N99/005 ,  G06F15/18

摘要： Method, system, and programs for distributed machine learning on a cluster including a plurality of nodes are disclosed. A machine learning process is performed in each of the plurality of nodes based on a respective subset of training data to calculate a local parameter. The training data is partitioned over the plurality of nodes. A plurality of operation nodes are determined from the plurality of nodes based on a status of the machine learning process performed in each of the plurality of nodes. The plurality of operation nodes are connected to form a network topology. An aggregated parameter is generated by merging local parameters calculated in each of the plurality of operation nodes in accordance with the network topology.

摘要翻译： 公开了包括多个节点在内的分布式机器学习的方法，系统和程序。 基于训练数据的相应子集，在多个节点的每一个中执行机器学习处理，以计算局部参数。 训练数据在多个节点上分区。 基于在多个节点中的每一个中执行的机器学习处理的状态，从多个节点确定多个操作节点。 多个操作节点被连接以形成网络拓扑。 通过根据网络拓扑结合在多个操作节点中的每一个中计算的局部参数来生成聚合参数。