Efficient Machine Learning for Big Data
TL;DR: This paper reviews the theoretical and experimental data-modeling literature, in large-scale data-intensive fields, and introduces new algorithmic approaches with the least memory requirements and processing to minimize computational cost, while maintaining/improving its predictive/classification accuracy and stability.
read more
About: This article is published in Big Data Research. The article was published on 01 Sep 2015. and is currently open access. The article focuses on the topics: Computational resource & Stability (learning theory).
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
The Role of Big Data and Machine Learning in COVID-19
TL;DR: The results showed that most of the countries in the time of Corona turned into smart cities, totally dependent on smart applications based on the analysis of BD using ML, and data privacy is one of the most important challenges facing data analysis.
Investigating the use of Bayesian networks for small dataset problems
Anastacia MacAllister
- 01 Jan 2018
TL;DR: This chapter discusses the development of Bayesian networks for small sample sizes with a focus on the manipulation of prior Probabilities.
7
Data-driven models & mathematical finance : apposition or opposition?
TL;DR: This thesis considers the impact of data-driven modelling transition in finance and explores the challenges associated with properly regulating the algorithmic trading markets, in the era of flash crashes, by formalizing a particle filter methodology.
7
Big Data in Supply Chain Management and Medicinal Domain
Aniket Nargundkar,Anand J. Kulkarni +1 more
- 01 Jan 2020
TL;DR: The overall process of big data analytics from data generation till data results visualization is exemplified and coming trends of bigData analytics with wearable or implanted sensors is explicated.
7
An Ensemble Approach to Predict Weather Forecast using Machine Learning
N. Sravanthi,M.Lohitha Venkat,S. Harshini,K. Ashesh +3 more
- 01 Sep 2020
TL;DR: Through the study it has been concluded to implement a proactive disaster recognition system to avoid the future loss of human lives and related environmental effect.
7
References
Reducing the Dimensionality of Data with Neural Networks
TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
•Book
Machine Learning : A Probabilistic Perspective
Kevin P. Murphy
- 24 Aug 2012
TL;DR: This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach, and is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
11.8K