Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/353459
Type: Artigo
Title: Kuaa: a unified framework for design, deployment, execution, and recommendation of machine learning experiments
Author: Weneck, Rafael de Oliveira
Almeida, Waldir Rodrigues de
Stein, Bernardo Vecchia
Pazinato, Daniel Vatanabe
Mendes Júnior, Pedro Ribeiro
Penatti, Otávio Augusto Bizetto
Rocha, Anderson
Torres, Ricardo da Silva
Abstract: In this work, we propose Kuaa, a workflow-based framework that can be used for designing, deploying, and executing machine learning experiments in an automated fashion. This framework is able to provide a standardized environment for exploratory analysis
Subject: Ciência - Experiências
Aprendizado de máquina
Fluxo de trabalho
Sistemas de recomendação (Filtragem da informação)
Science - Experiments
Machine learning
Workflow
Recommender systems (Information filtering)
Country: Países Baixos
Editor: Elsevier
Rights: Fechado
Identifier DOI: 10.1016/j.future.2017.06.013
Address: https://www.sciencedirect.com/science/article/pii/S0167739X17301565
Date Issue: 2018
Appears in Collections:IC - Artigos e Outros Documentos

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