359,866 results for machine learning
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Journal Article Full text
Machine Learning Meets Cancer
SIMPLE SUMMARY: This review examines the latest technologies using machine learning (ML) methods, including the use of convolutional neural networks, decision trees, and generative adversarial networks to solve problems concerning cancer recognition, planning optimal treatment strategies, as well…
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Journal Article Full text
Machine Learning in Bioelectrocatalysis
…experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the…
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Journal Article Full text
Federated Quantum Machine Learning
…One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done…
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Journal Article Full text
Machine Learning Force Fields
[Image: see text] In recent years, the use of machine learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of…
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Explanatory Interactive Machine Learning
The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper…
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Lifelong Machine Learning Potentials
[Image: see text] Machine learning potentials (MLPs) trained on accurate quantum chemical data can retain the high accuracy, while inflicting little computational demands. On the downside, they need to be trained for each individual system. In recent years, a vast number of MLPs have been trained…
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Machine learning for microbiologists
Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used…
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Predicting ACL Reconstruction Failure with Machine Learning: Development of Machine Learning Prediction Models
…PURPOSE: To (1) evaluate the predictive performance of different machine learning algorithms for the occurrence of failure in ACLR and (2) identify the most relevant predictors associated with this outcome. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: A total of 680 patients who…
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Book Full text
Machine Learning: de la théorie à la pratique
…élèves ingénieurs. La première édition, connue sous le nom Apprentissage machine, fut traduite en chinois par les éditions iTuring. Dans cette deuxième édi-tion, un nouveau chapitre est dédié au Deep Learning, sur les réseaux de neurones artificiels, et nous avons réorganisé les autres chapitres…
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Book Full text
Big Data and Machine Learning in Quantitative Investment
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment. Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and…
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Journal Article
Machine learning:
…type of fuel, (pine and cork oak trees, Matorral, …). The 3 types of machine learning models (binary logistic regression, random forest and XG Boost) have shown very interesting results in terms of forest fire predictability by correctly classifying an average of 85% of the sample reserved for…
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Journal Article
Machine learning:
…type of fuel, (pine and cork oak trees, Matorral, …). The 3 types of machine learning models (binary logistic regression, random forest and XG Boost) have shown very interesting results in terms of forest fire predictability by correctly classifying an average of 85% of the sample reserved for…
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Journal Article Full text
Exploration and Exploitation: An Essay on (Machine) Learning, Algorithms, and Information Provision
…or try new options that may yield better results. Work in organizational learning describes this problem as the exploration-exploitation dilemma. Understanding and addressing that dilemma has become a key part of an algorithmic approach to computation, machine learning, as it is applied to…
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Journal Article Full text
BEYOND DATA DELETION: ADDRESSING ANTICOMPETITIVE CONDUCT IN THE ERA OF MACHINE LEARNING
…that their army of data scientists can make sense of all the noise using machine learning. Potential profits encourage companies to seek out data—sometimes unlawfully through collusion. The use of unlawfully collected data in algorithms harms competition. Such data provides a competitive edge that…
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Beyond the Iudex Threshold: Human Oversight as the Conscience of Machine Learning
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Thesis / Dissertation Full text
Klasifikasi Penjurusan Siswa Sma Dengan Menggunakan Algoritma Machine Learning
…dalam proses penjurusan maka dimanfaatkannya algoritma klasifikasi machine learning dalam proses penjurusan siswa. Dalam melakukan klasifikasi machine learning, penelitian ini menggunakan empat algoritma machine learning yaitu K-Nearest Neighbors, Support Vector Machine, Random Forest dan Naïve…
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