Kernel methods library for pattern analysis and machine learning in python
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.
Code (1)
Tasks
BIG-bench Machine LearningDomain AdaptationSimilar Papers 제목 키워드 기반
Machine Learning (ML) library in Linux kernel
Linux kernel is a huge code base with enormous number of subsystems and possible configuration options that results in unmanageable complexity of elaborating an efficient configuration. Machine Learning (ML) is approach/…
Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons
Graph kernels are powerful tools to bridge the gap between machine learning and data encoded as graphs. Most graph kernels are based on the decomposition of graphs into a set of patterns. The similarity between two graph…
BIG-bench Machine LearningGraph ClassificationTorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
TorchKM is an open-source library for kernel machines, including support vector machines, kernel logistic regression, and kernel quantile regression, with GPU acceleration. The library features a scikit-learn-style API a…
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available Python library for e…
Argument MiningBIG-bench Machine LearningMachine TranslationSpam detectionsQUlearn -- A Python Library for Quantum Machine Learning
sQUlearn introduces a user-friendly, NISQ-ready Python library for quantum machine learning (QML), designed for seamless integration with classical machine learning tools like scikit-learn. The library's dual-layer archi…
Quantum Machine Learning