Online machine learning ≈ Online machine learning
View article
Machine learning for combinatorial optimization: A methodological tour d'horizon Open
This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-…
View article
Federated Learning: Collaborative Machine Learning withoutCentralized Training Data Open
Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm without transferring data samples across numerous decentralized edge devices or servers. This strategy differs from standard…
View article
Machine learning algorithms review Open
Machine learning is a field of study where the computer can learn for itself without a human explicitly hardcoding the knowledge for it. These algorithms make up the backbone of machine learning. This paper aims to study the field of machi…
View article
Quantum-Enhanced Machine Learning Open
The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work …
View article
Optimization Methods for Large-Scale Machine Learning Open
This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural …
View article
Diversity in Machine Learning Open
Machine learning methods have achieved good performance and been widely applied in various real-world applications. They can learn the model adaptively and be better fit for special requirements of different tasks. Generally, a good machin…
View article
A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions Open
Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. Machine…
View article
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning Open
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable…
View article
Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion Open
Path loss prediction is of great significance for the performance optimization of wireless networks. With the development and deployment of the fifth-generation (5G) mobile communication systems, new path loss prediction methods with high …
View article
SUPERVISED MACHINE LEARNING APPROACHES: A SURVEY Open
One of the core objectives of machine learning is to instruct computers to use data or past experience to solve a given problem. A good number of successful applications of machine learning exist already, including classifier to be trained…
View article
Deep Extreme Learning Machine and Its Application in EEG Classification Open
Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and allev…
View article
Online Learning: A Comprehensive Survey Open
Online learning represents an important family of machine learning algorithms, in which a learner attempts to resolve an online prediction (or any type of decision-making) task by learning a model/hypothesis from a sequence of data instanc…
View article
Breast Cancer Prediction using varying Parameters of Machine Learning Models Open
Malignancy of tumor has caused major number of deaths among women. Machine learning tools with proper hyper parametric can help in identifying tumors efficiently. This paper presents six supervised machine learning algorithms such as k-Nea…
View article
The Value of Collaboration in Convex Machine Learning with Differential Privacy Open
In this paper, we apply machine learning to distributed private data owned by multiple data owners, entities with access to non-overlapping training datasets. We use noisy, differentially-private gradients to minimize the fitness cost of t…
View article
The Benefit of Multitask Representation Learning Open
We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case…
View article
Ensemble Transfer Learning Algorithm Open
Transfer learning and ensemble learning are the new trends for solving the problem that training data and test data have different distributions. In this paper, we design an ensemble transfer learning framework to improve the classificatio…
View article
Transfer Learning and Meta Learning-Based Fast Downlink Beamforming Adaptation Open
peer reviewed
View article
Online Meta-Learning Open
A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this …
View article
Research on machine learning framework based on random forest algorithm Open
With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine le…
View article
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm Open
Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the param…
View article
Learning Cumulatively to Become More Knowledgeable Open
In classic supervised learning, a learning algorithm takes a fixed training data of several classes to build a classifier. In this paper, we propose to study a new problem, i.e., building a learning system that learns cumulatively. As time…
View article
Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning Open
The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]…
View article
An Overview of Machine Learning-Based Techniques for Solving Optimization Problems in Communications and Signal Processing Open
Despite the growing interest in the interplay of machine learning and optimization, existing contributions remain scattered across the research board, and a comprehensive overview on such reciprocity still lacks at this stage. In this cont…
View article
Adaptive Federated Learning in Resource Constrained Edge Computing Systems Open
Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable…
View article
Combining supervised and unsupervised machine learning algorithms to predict the learners’ learning styles Open
The implementation of an efficient adaptive e-learning system requires the construction of an effective student model that represents the student’s characteristics, among those characteristics, there is the learning style that refers to th…
View article
A Semi-Supervised Extreme Learning Machine Algorithm Based on the New Weighted Kernel for Machine Smell Open
At present, machine sense of smell has shown its important role and advantages in many scenarios. The development of machine sense of smell is inseparable from the support of corresponding data and algorithms. However, the process of olfac…
View article
A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis Open
COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spr…
View article
Online Learning Rate Adaptation with Hypergradient Descent Open
We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness of the method in a range of optimization problems by apply…
View article
A Convex Formulation for Learning from Crowds Open
Recently crowdsourcing services are often used to collect a large amount of labeled data for machine learning, since they provide us an easy way to get labels at very low cost and in a short period. The use of crowdsourcing has introduced …
View article
Machine Learning: A Review of Learning Types Open
In this paper, various machine learning techniques are discussed. These algorithms are used for many applications which include data classification, prediction, or pattern recognition. The primary goal of machine learning is to automate hu…