Deep learning applications and challenges in big data analytics
MM Najafabadi, F Villanustre, TM Khoshgoftaar… - Journal of big …, 2015 - Springer
Abstract Big Data Analytics and Deep Learning are two high-focus of data science. Big Data
has become important as many organizations both public and private have been collecting …
has become important as many organizations both public and private have been collecting …
Selecting influential examples: Active learning with expected model output changes
In this paper, we introduce a new general strategy for active learning. The key idea of our
approach is to measure the expected change of model outputs, a concept that generalizes …
approach is to measure the expected change of model outputs, a concept that generalizes …
A benchmark and comparison of active learning for logistic regression
Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …
Nonparametric part transfer for fine-grained recognition
In the following paper, we present an approach for fine-grained recognition based on a new
part detection method. In particular, we propose a nonparametric label transfer technique …
part detection method. In particular, we propose a nonparametric label transfer technique …
Batch mode active learning for regression with expected model change
While active learning (AL) has been widely studied for classification problems, limited efforts
have been done on AL for regression. In this paper, we introduce a new AL framework for …
have been done on AL for regression. In this paper, we introduce a new AL framework for …
Using big data to improve the performance management: a case study from the UAE FM industry
M Mawed, A Al-Hajj - Facilities, 2017 - emerald.com
Purpose This paper aims to explore how big data analytics (BDA) collected and stored
through specific data software [Construction Operations Building Information Exchange …
through specific data software [Construction Operations Building Information Exchange …
Knowledge augmented machine learning with applications in autonomous driving: A survey
The availability of representative datasets is an essential prerequisite for many successful
artificial intelligence and machine learning models. However, in real life applications these …
artificial intelligence and machine learning models. However, in real life applications these …
Active learning and discovery of object categories in the presence of unnameable instances
Current visual recognition algorithms are" hungry" for data but massive annotation is
extremely costly. Therefore, active learning algorithms are required that reduce labeling …
extremely costly. Therefore, active learning algorithms are required that reduce labeling …
Change detection using high resolution remote sensing images based on active learning and Markov random fields
Change detection has been widely used in remote sensing, such as for disaster assessment
and urban expansion detection. Although it is convenient to use unsupervised methods to …
and urban expansion detection. Although it is convenient to use unsupervised methods to …
Optimised probabilistic active learning (OPAL) for fast, non-myopic, cost-sensitive active classification
In contrast to ever increasing volumes of automatically generated data, human annotation
capacities remain limited. Thus, fast active learning approaches that allow the efficient …
capacities remain limited. Thus, fast active learning approaches that allow the efficient …