This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems.

Algorithm engineering tightly integrates modeling, algorithm design, analysis, imple-mentation and experimental evaluation into a cycle resembling the scientific method used in the natural sciences. Reusable, robust, flexible, and efficient implementations are put into algorithm libraries.

Jan 14, 2024 · Big data analytics consists of numerous techniques from statistical modeling to machine learning to natural language processing that use algorithms to find hidden patterns, associations, anomalies and trends within massive datasets.

Jan 26, 2021 · In light of this debate, this work investigates the issue of algorithms’ data hungriness. First, it surveys the issue from different perspectives. Then, it presents a comprehensive review of existing data-efficient methods …

This course focuses on algorithms that are specifically designed for large datasets and will cover the following topics. Some of the new computational models that capture various aspects of massive data computation such as streaming algorithms, and sub-linear time algorithms.

Jan 12, 2025 · With the help of algorithm analysis and experimental findings from the benchmark database Caltech 101, a successful method for large-scale image classification is developed and put forth in the context of big data.

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 can...

Understanding essential big data algorithms is key to effective analytics and visualization. These algorithms, like MapReduce and Apache Spark, help process and analyze massive datasets, enabling insights that drive decision-making and enhance …

Models of Computation for Big Data, covers mathematical models for developing such algorithms, which has its roots in the study of big data that occur often in various applications. Most techniques discussed come from research in the last decade.

Specifically, we considered that new research in the Big Data scenario for fuzzy modeling must be focused on re-designing the state-of-the-art algorithms, as well as novel approaches for recent work scenarios.