Sep 30, 2015 · In this paper, we report a hybrid approach called weighted parameter averaging (WPA), which optimizes the regularized hinge loss with respect to ...

In this paper, we propose a hybrid approach which uses weighted parameter averaging and learns the weights in a distributed manner from the data.In particular, ...

Two popular approaches for distributed training of SVMs on big data are parameter averaging and ADMM. Parameter averaging is efficient but suffers from loss of ...

Distributed Weighted Parameter Averaging for SVM Training on Big Data. June 20, 2017. Authors. Track: Papers. Downloads: Download PDF. Topics: ...

In this paper, we report a hybrid approach called weighted parameter averaging (WPA), which optimizes the regularized hinge loss with respect to weights on ...

Ayan Das, Raghuveer Chanda, Smriti Agrawal, Sourangshu Bhattacharya : Distributed Weighted Parameter Averaging for SVM Training on Big Data.

Mar 25, 2024 · Distributed machine learning training is a powerful technique that allows us to train models on large datasets by distributing the workload across multiple ...

We propose. Projection-SVM, a distributed implementation of kernel support vector machine for large datasets using subspace partitioning. In subspace ...

In this article, we introduce Markov sampling and different weights for distributed learning with the classical support vector machine (cSVM). We first estimate ...

When you first want to do a SGD in Spark MLlib, the driver first broadcasts the model to executors; then when executors get the model from the driver, they use ...