论文标题
内核方法库用于模式分析和机器学习
Kernel methods library for pattern analysis and machine learning in python
论文作者
论文摘要
事实证明，内核方法是多种域中模式分析和机器学习（ML）的强大技术。但是，他们的许多原始或高级实现仍保留在MATLAB中。随着Python在ML和数据科学界的令人难以置信的兴起和采用，显然需要一个定义明确的库，不仅可以使用流行的内核，而且还可以轻松地将定制内核的定义定义为各种应用程序。 kernelmethods库填充了域 - 不平衡方式的Python ML生态系统中的重要空隙，从而使样本数据类型可以是数值，分类，图形或它们组合的任何内容。此外，该库提供了许多定义明确的类，以使各种基于内核的操作有效（大规模数据集），模块化（用于域名适应性）和可行的操作（跨不同的生态系统）。该库可在https://github.com/raamana/kernelmethods上找到。
Kernel methods have proven to be powerful techniques for pattern analysis and machine learning (ML) in a variety of domains. However, many of their original or advanced implementations remain in Matlab. With the incredible rise and adoption of Python in the ML and data science world, there is a clear need for a well-defined library that enables not only the use of popular kernels, but also allows easy definition of customized kernels to fine-tune them for diverse applications. The kernelmethods library fills that important void in the python ML ecosystem in a domain-agnostic fashion, allowing the sample data type to be anything from numerical, categorical, graphs or a combination of them. In addition, this library provides a number of well-defined classes to make various kernel-based operations efficient (for large scale datasets), modular (for ease of domain adaptation), and inter-operable (across different ecosystems). The library is available at https://github.com/raamana/kernelmethods.
