Machine learning articles within Nature Methods Featured Article|06 June 2025 Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression Spotiflow uses
All you need to learn machine learning in 2024 is a laptop and a list of the steps you need to take. I'm a student researcher working for an ex-meta professor and have had interviews with Google DeepMind, Amazon, and other cool companies, but it took me over 3 years to get to this...
3.2.3Machine learning methods Currently, themachine learning methodis favored for predictingleak locationin the pipes[104], as it can distill information from massive amounts of raw data[105]. It accurately locates the leakage point and calculates the damage size by analyzing previously obtained sci...
Many industries that work with large volumes of data see the value in using machine learning technology to boost productivity. Machine learning is not a replacement for humans but rather a tool that helps to extract information quickly and accurately so that humans can evaluate the recommended actio...
Machine Learning -- 主动学习(AL) 参考论文:Survey on active learning algorithms. Computer Engineering and Applications 主动学习算法作为构造有效训练集的方法,其目标是通过迭代抽样,寻找有利于提升分类效果的样本,进而减少分类训练集的大小,在有限的时间和资源的前提下,提高分类算法的效率。主动学习已成为模式识别、...
Identifying therapeutic targets is challenging, especially for orphan diseases. Here, the authors integrate GWAS and TWAS with machine learning methods to predict therapeutic targets for various diseases and demonstrate the usefulness in practice.
机器学习（Machine Learning）的由来 1.机器学习的历史 你可能会觉得机器学习、人工智能和智能计算机的历史都是最近的事情。当我们想到这些技术时，我们往往会想到一些非常现代的东西，一些只在过去的十年里才发展起来的东西。但你可能会感到惊讶，机器学习的历史可以追溯到20世纪40年代。虽然我们无法确切指出机器学习是...
新书推荐 | Machine Learning Methods 李航2024最新神作《机器学习方法》重磅来袭! {PDF版领取方式在文末} 书中部分内容展示: 总结: 不得不说,2024 版的《机器学习方法》绝对是一本令人惊艳的机器学习教材。它内容全面,涵盖了机器学习领域的各个方面;体系系统,从基础理论到实际应用,层层递进;实用性强,通过丰富的...
3.2重采样方法(Resampling methods)：从训练数据集上重复采样得到多组训练样本，对每组样本拟合一个模型...
Machine Learning -- 11种相似性度量方法(总结版) 在做分类时常常需要估算不同样本之间的相似性度量(Similarity Measurement),这时通常采用的方法就是计算样本间的“距离”(Distance)。采用什么样的方法计算距离是很讲究,甚至关系到分类的正确与否。 本文的目的就是对常用的相似性度量作一个总结。