204,674 results for machine learning
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Journal Article Full text
Machine learning code snippets semantic classification
…snippets extracted from Code4ML automatically. Code4ML is a comprehensive machine learning code corpus compiled from Kaggle, a renowned data science competition platform. The corpus includes code snippets and information about the respective kernels and competitions, but it is limited in the…
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A Surgeon’s Guide to Machine Learning
Machine learning (ML) represents a collection of advanced data modeling techniques beyond the traditional statistical models and tests with which most clinicians are familiar. While a subset of artificial intelligence, ML is far from the science fiction impression frequently associated with AI. At…
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Machine Learning the Warm Rain Process
…relationships for large‐scale models to be computationally efficient. Machine learning provides another potential tool to improve our empirical parameterizations of clouds. To explore these opportunities, we replace the warm rain formation process in a General Circulation Model (GCM) with a…
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QDataSet, quantum datasets for machine learning
Measurement(s) Simulations of one- and two-qubit quantum systems evolving in the presence and absence of noise and distortion Technology Type(s) Simulated measurement using Python packages Sample Characteristic - Organism Simulated quantum systems Sample Characteristic - Environment Quantum systems…
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Fake News Detection using Machine Learning
…work provides a solution by introducing a fake news detection model using machine learning. This model requires prerequisite data extracted from various news websites. Web scraping technique is used for data extraction which is further used to create datasets. The data is classified into two major…
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Molecular machine learning with conformer ensembles
Virtual screening can accelerate drug discovery by identifying promising candidates for experimental evaluation. Machine learning is a powerful method for screening, as it can learn complex structure–property relationships from experimental data and make rapid predictions over virtual libraries…
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Discharge estimation based on machine learning
To overcome the limitations of the traditional stage-discharge models in describing the dynamic characteristics of a river, a machine learning method of non-parametric regression, the locally weighted regression method was used to estimate discharge. With the purpose of improving the precision and…
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Machine learning assisted empirical formula augmentation
…we proposed an augmentation strategy of empirical formula based on a machine learning method to build the relationship between martensite transformation start temperature (Ms) and compositions in Cu-Al-based SMA system. A series of ML models were established by physical and chemical features and a…
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Diagnosing Breast Cancer by Machine Learning
…to diagnose cancer, Decision Tree, Random Forest, and Support Vector Machine methods were used as machine learning methods. Results: In the first step, by content analysis method, 29 effective factors in Breast cancer were identified. Then, taking into account the indigenous and climatic conditions…
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Using Machine Learning for Precision Measurements
The use of machine learning techniques for classification is well established. They are applied widely to improve the signal-to-noise ratio and the sensitivity of searches for new physics at colliders. In this study I explore the use of machine learning for optimizing the output of high precision…
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Solving musculoskeletal biomechanics with machine learning
Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which…
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Machine learning Calabi-Yau four-folds
Hodge numbers of Calabi-Yau manifolds depend non-trivially on the underlying manifold data and they present an interesting challenge for machine learning. In this letter we consider the data set of complete intersection Calabi-Yau four-folds, a set of about 900,000 topological types, and study…
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Radical empiricism and machine learning research
I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views…
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Machine learning and complex biological data
Abstract Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
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A Developmental Approach to Machine Learning?
Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems…
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Universal activation function for machine learning
Abstract This article proposes a universal activation function (UAF) that achieves near optimal performance in quantification, classification, and reinforcement learning (RL) problems. For any given problem, the gradient descent algorithms are able to evolve the UAF to a suitable activation…
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Machine learning for modeling animal movement.
…effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that…
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