Computational learning theory
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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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Small data machine learning in materials science Open
This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of deali…
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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…
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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 …
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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…
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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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A Survey on Machine Learning: Concept,Algorithms and Applications Open
Over the past few decades, Machine Learning (ML) has evolved from the endeavour of few computer enthusiasts exploiting the possibility of computers learning to play games, and a part of Mathematics (Statistics) that seldom considered compu…
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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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Machine Learning: Quantum vs Classical Open
Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. Th…
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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…
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A Research on Machine Learning Methods and Its Applications Open
Machine learning is a science which was found and developed as a subfield of artificial intelligence in the 1950s. The first steps of machine learning goes back to the 1950s but there were no significant researches and developments on this…
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Deep dive into machine learning density functional theory for materials science and chemistry Open
With the growth of computational resources, the scope of electronic structure\nsimulations has increased greatly. Artificial intelligence and robust data\nanalysis hold the promise to accelerate large-scale simulations and their\nanalysis …
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Engineering problems in machine learning systems Open
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems that employ machine learning and deep learning models, such as automated driving vehicles. In order to use machine learning in a safety-critical sys…
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The virtue of simplicity: On machine learning models in algorithmic trading Open
Machine learning models are becoming increasingly prevalent in algorithmic trading and investment management. The spread of machine learning in finance challenges existing practices of modelling and model use and creates a demand for pract…
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Machine Learning and Deep Learning Open
Now-a-days artificial intelligence has become an asset for engineering and experimental studies, just like statistics and calculus. Data science is a growing field for researchers and artificial intelligence, machine learning and deep lear…
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Deep Machine Learning and Neural Networks: An Overview Open
Deep learning is a technique of machine learning in artificial intelligence area. Deep learning in a refined "machine learning" algorithm that far surpasses a considerable lot of its forerunners in its capacities to perceive syllables and …
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Quantum Machine Learning: Scope for real-world problems Open
Quantum computing with its inherent parallelism provides a quantum advantage over classical computing. Its potential to offer breakthrough advances in various areas of science and engineering is foreseen. Machine learning is one of the key…
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Learning invariants using decision trees and implication counterexamples Open
Inductive invariants can be robustly synthesized using a learning model where the teacher is a program verifier who instructs the learner through concrete program configurations, classified as positive, negative, and implications. We propo…
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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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Performance Analysis on Machine Learning-Based Channel Estimation Open
Recently, machine learning-based channel estimation has attracted much\nattention. The performance of machine learning-based estimation has been\nvalidated by simulation experiments. However, little attention has been paid to\nthe theoreti…
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On-Device Machine Learning: An Algorithms and Learning Theory Perspective Open
The predominant paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with increasing number of smart devices and improved hardware, th…
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A Survey of Optimization Methods from a Machine Learning Perspective Open
Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponen…
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Research Methods in Machine Learning: A Content Analysis Open
Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learnin…
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Human and Machine Learning Open
In this paper, we consider learning by human beings and machines in the light of Herbert Simon’s pioneering contributions to the theory of Human Problem Solving. Using board games of perfect information as a paradigm, we explore difference…
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Beneficial and harmful explanatory machine learning Open
Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine…
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Machine learning in tutorials – Universal applicability, underinformed application, and other misconceptions Open
Machine learning has become a key component of contemporary information systems. Unlike prior information systems explicitly programmed in formal languages, ML systems infer rules from data. This paper shows what this difference means for …
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Learning Curves for Decision Making in Supervised Machine Learning: A Survey Open
Learning curves are a concept from social sciences that has been adopted in the context of machine learning to assess the performance of a learning algorithm with respect to a certain resource, e.g., the number of training examples or the …
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Computational Power and the Social Impact of Artificial Intelligence Open
Machine learning is a computational process. To that end, it is inextricably tied to computational power - the tangible material of chips and semiconductors that the algorithms of machine intelligence operate on. Most obviously, computatio…
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MODEL THEORY AND MACHINE LEARNING Open
About 25 years ago, it came to light that a single combinatorial property determines both an important dividing line in model theory (NIP) and machine learning (PAC-learnability). The following years saw a fruitful exchange of ideas betwee…
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Machine Learning with a Reject Option: A survey Open
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can have severe consequences. Albeit already studied in 1970, …