3,327 results for machine learning
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Quantum machine learning beyond kernel methods
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of quantum machine learning models have been introduced and studied extensively. Yet, our understanding of how these models…
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Clinical applications of continual learning machine learning
With advances in artificial intelligence (AI), particularly in machine learning and deep learning, the potential uses for AI in medicine are growing. Continual learning, also known as lifelong learning or online machine learning, is a fundamental idea in machine learning in which models…
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Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
…it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of…
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Explainable Machine Learning in Credit Risk Management
Abstract The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so…
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COVID-19 Outbreak Prediction with Machine Learning
…models need to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine…
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Predicting property prices with machine learning algorithms
This study uses three machine learning algorithms including, support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices. It applies these methods to examine a data sample of about 40,000 housing transactions in a period of over 18 years…
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Attention in Psychology, Neuroscience, and Machine Learning
…including awareness, vigilance, saliency, executive control, and learning. It has also recently been applied in several domains in machine learning. The relationship between the study of biological attention and its use as a tool to enhance artificial neural networks is not always clear. This…
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Stroke Risk Prediction with Machine Learning Techniques
…and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high…
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Machine Learning in Chemical Engineering: A Perspective
…(bio‐)catalysts, and functional materials. Recent breakthroughs in machine learning (ML) provide unique opportunities, but only joint interdisciplinary research between the ML and chemical engineering (CE) communities will unfold the full potential. We identify six challenges that will open new…
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Machine Learning for Cultural Heritage: A Survey
The application of Machine Learning (ML) to Cultural Heritage (CH) has evolved since basic statistical approaches such as Linear Regression to complex Deep Learning models. The question remains how much of this actively improves on the underlying algorithm versus using it within a ‘black box’…
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An Unsupervised Machine Learning Algorithms: Comprehensive Review
Machine learning (ML) is a data-driven strategy in which computers learn from data without human intervention.The outstanding ML applications are used in a variety of areas.In ML, there are three types of learning problems: Supervised, Unsupervised, and Semi-Supervised Learning.Examples of…
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Combustion machine learning: Principles, progress and prospects
…for extracting new knowledge and insights—if harnessed effectively. Machine learning (ML) techniques have demonstrated remarkable success in data analytics, thus offering a new paradigm for data-intense analyses and scientific investigations through combustion machine learning (CombML). While…
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Forecasting Corn Yield With Machine Learning Ensembles
…to more accurately predict crop yields. Recent research has shown that Machine learning (ML) can provide reasonable predictions, faster, and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a “committee” of models (machine…
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