Learning classifier system
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Q-Learning Algorithms: A Comprehensive Classification and Applications Open
Q-learning is arguably one of the most applied representative reinforcement learning approaches and one of the off-policy strategies. Since the emergence of Q-learning, many studies have described its uses in reinforcement learning and art…
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Learning how to Active Learn: A Deep Reinforcement Learning Approach Open
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limite…
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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…
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Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms Open
There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic …
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Adversarial environment reinforcement learning algorithm for intrusion detection Open
Producción Científica
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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…
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eXtreme Gradient Boosting Algorithm with Machine Learning: a Review Open
The primary task of machine learning is to extract valuable information from the data that is generated every day, process it to learn from it, and take useful actions. Original language process, pattern detection, search engines, medical …
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An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges Open
Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated lin…
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Reinforcement Learning in Financial Markets Open
Recently there has been an exponential increase in the use of artificial intelligence for trading in financial markets such as stock and forex. Reinforcement learning has become of particular interest to financial traders ever since the pr…
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Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers Open
The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity fe…
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Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis Open
Deep learning represents a powerful set of techniques for profiling sidechannel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong att…
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Applying Machine Learning in Self-adaptive Systems Open
Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system du…
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Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification Open
Most existing person re-identification(Re-ID) approaches achieve superior results based on the assumption that a large amount of pre-labelled data is usually available and can be put into training phrase all at once. However, this assumpti…
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A Study on the Evolution of Ransomware Detection Using Machine Learning and Deep Learning Techniques Open
This survey investigates the contributions of research into the detection of ransomware malware using machine learning and deep learning algorithms. The main motivations for this study are the destructive nature of ransomware, the difficul…
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Research and Implementation of Machine Learning Classifier Based on KNN Open
Machine learning classifier is an important part of pattern recognition system; it is also an important research field of machine learning. The main research object of this paper is K data mining (KNN, K Nearest Neighbor) classification me…
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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…
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Reinforcement Learning Algorithms: An Overview and Classification Open
The desire to make applications and machines more intelligent and the\naspiration to enable their operation without human interaction have been\ndriving innovations in neural networks, deep learning, and other machine\nlearning techniques.…
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A New Random Forest Algorithm Based on Learning Automata Open
The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more tha…
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AWAC: Accelerating Online Reinforcement Learning with Offline Datasets Open
Reinforcement learning (RL) provides an appealing formalism for learning control policies from experience. However, the classic active formulation of RL necessitates a lengthy active exploration process for each behavior, making it difficu…
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Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction Open
Machine learning is often perceived as a sophisticated technology accessible only by highly trained experts. This prevents many physicians and biologists from using this tool in their research. The goal of this paper is to eliminate this o…
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Automating post-exploitation with deep reinforcement learning Open
In order to assess the risk of information systems, it is important to investigate the behavior of the attacker after successful exploitation (post-exploitation). However, the audit requires the experts, and to the best of our knowledge, t…
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Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning Open
Learning activities are considerably supported and improved by the rapid advancement of e-learning systems. This gives students a tremendous chance to participate in learning activities worldwide. The Massive Open Online Courses (MOOCs) pl…
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A Deep Reinforcement Learning Approach for Early Classification of Time Series Open
International audience
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Unsupervised Meta-Learning for Reinforcement Learning Open
Meta-learning algorithms use past experience to learn to quickly solve new tasks. In the context of reinforcement learning, meta-learning algorithms acquire reinforcement learning procedures to solve new problems more efficiently by utiliz…
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Darknet traffic classification and adversarial attacks using machine learning Open
The anonymous nature of darknets is commonly exploited for illegal activities. Previous research has employed machine learning and deep learning techniques to automate the detection of darknet traffic in an attempt to block these criminal …
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Classification of Diseases Using Machine Learning Algorithms: A Comparative Study Open
Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health profess…
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Research on Machine Learning and Its Algorithms and Development Open
This article analyzes the basic classification of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It combines analysis on common algorithms in machine learning, such as decision tree algo…
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Learning how to Active Learn: A Deep Reinforcement Learning Approach Open
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate. This is usually done using heuristic selection methods, however the effectiveness of such methods is limite…
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Multi-label learning for improving discretely-modulated continuous-variable quantum key distribution Open
We propose a novel scheme for discretely-modulated continuous-variable quantum key distribution (CVQKD) using machine learning technologies, which called multi-label learning-based CVQKD (ML-CVQKD). In particular, the proposed scheme divid…
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Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water Open
A general formula with high generalization and accurate prediction power is highly desirable for science, technology and engineering. In addition to human beings, artificial intelligence algorithms show great promise for the discovery of f…