Automated Machine Learning: Methods, Systems, Challenges
时间: 2024-05-19 20:12:24 浏览: 11
Automated Machine Learning refers to the automation of the repetitive and time-consuming tasks involved in traditional machine learning workflows. It involves using algorithms and tools to automate tasks like dataset cleaning, algorithm selection, hyperparameter tuning, and model selection. The use of Automated Machine Learning can speed up the time to create and deploy machine learning models and make it more accessible to non-experts. However, there are still many challenges to overcome in the development of Automated Machine Learning, including scalability, interpretability, and fairness.
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Network Slicing in 5G: Survey and Challenges解决问题
网络切片在5G中的应用和挑战可以解决以下问题:
1. 服务定制化:网络切片可以根据不同应用的需求,提供个性化的服务。例如,对于增强移动宽带应用,网络切片可以为高带宽、低延迟的应用提供专用的网络资源,以保证用户体验。
2. 资源优化:通过网络切片,网络资源可以有效地分配和管理。不同切片可以共享底层网络资源,提高资源利用率。同时,网络切片可以根据需求动态分配和释放资源,以适应不同应用的变化需求。
3. 管理和协调:网络切片需要有效的管理和协调机制,以确保各个切片之间的互操作性和资源冲突的解决。这包括切片的创建、配置、监控和维护等方面。
4. 安全性:网络切片需要保障数据传输的安全性和隐私保护。由于不同切片之间共享底层网络资源,安全隔离和数据保护是一个重要的挑战。
5. 部署和维护:网络切片的部署和维护需要考虑物理基础设施、网络拓扑和配置等方面的问题。同时,还需要解决切片之间的互操作性和版本兼容性的问题。
通过研究网络切片的应用和挑战,可以提供解决方案来改进5G网络的性能、灵活性和可管理性。
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Pattern Recognition and Machine Learning-01-Preface
The preface of the book "Pattern Recognition and Machine Learning" by Christopher Bishop provides an overview of the field of pattern recognition and machine learning, and the motivation behind writing the book. In the preface, Bishop highlights the importance of pattern recognition and machine learning in a wide range of applications, including computer vision, speech recognition, natural language processing, and robotics. He also discusses the challenges and opportunities in this field, such as dealing with high-dimensional data and designing effective algorithms.
Bishop explains that the book is intended to be a comprehensive introduction to the field of pattern recognition and machine learning, suitable for both students and researchers. The book covers a range of topics, including Bayesian decision theory, linear models for regression and classification, neural networks, kernel methods, clustering, and dimensionality reduction. Bishop emphasizes the importance of a probabilistic approach, and how it can be used to unify many of the different techniques used in pattern recognition and machine learning.
The preface concludes by acknowledging the contributions of many researchers in the field, and expressing the hope that the book will help to inspire and educate the next generation of researchers in pattern recognition and machine learning.
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