May 28, 2016 · In this section, we analyze the relationships between machine learning and SP techniques for big data processing from four perspectives: (1) statistical learning for big data analysis, (2) convex optimization for big data analytics, (3) stochastic approximation for big data analytics, and (4) outlying sequence detection for big data.

machine learning algorithms to work with large datasets: examples are new processing paradigms such as MapReduce [12] and distributed processing frameworks such as Hadoop [13]. Branches of machine learning including deep and online learning have also been adapted in an effort to overcome the challenges of machine learning with Big Data.

Jan 7, 2014 · This article has provided a comprehensive review of the three generations of tools/paradigms that realize machine learning algorithms for big data: The first-generation tools, which include SAS and SPSS, can help in deep analytics but may only scale vertically.

First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning.

Aug 14, 2020 · By feeding big data to a machine-learning algorithm, we might expect to see defined and analyzed results, like hidden patterns and analytics, that can assist in predictive modeling. For some companies, these algorithms might automate processes that were previously human-centered.

Dec 15, 2020 · The accepted chapters covered many themes including artificial intelligence and data mining applications, machine learning and applications, deep learning technology for big data analytics, and modeling, simulation, and security with big data. It is a valuable resource for researchers in the area of big data analytics and its applications.

Oct 1, 2015 · In the big data classification section, the machine learning processes, the classification modeling that is characterized by the big data controllers, and the classification algorithms that...

In this paper, Hadoop and Spark frameworks, the big data processing platforms, are evaluated and compared in terms of runtime, memory and network usage, and central processor efficiency. Hence, the K-nearest neighbor (KNN) algorithm is implemented on datasets with different sizes within both Hadoop and Spark frameworks.

Mar 20, 2023 · In this paper, we examine challenges of machine learning models in processing big data. These include the inherent uncertainty in data collection and questionable validity of machine learning model outcome.

Jan 26, 2018 · PDF | This paper discusses the role of Machine Learning (ML) based algorithms and methods in Big Data Processing & Analytics (BDA).