In structure mining, a graph kernel is a kernel function that computes an inner product on graphs. Graph kernels can be intuitively understood as functions measuring the similarity of pairs of graphs. They allow kernelized learning algorithms such as support vector machines to work directly on graphs, without having to do feature extraction to transform them to fixed-length, real-valued feature vectors. They find applications in bioinformatics, in chemoinformatics (as a type of molecule kernels), and in social network analysis. Concepts of graph kernels have been around since the 1999, when D. Haussler introduced convolutional kernels on discrete structures. The term graph kernels was more officially coined in 2002 by R. I. Kondor and J. Laffertyas kernels on graphs, i.e. similarity functions between the nodes of a single graph, with the World Wide Web hyperlink graph as a suggested application. In 2003, Gaertner et al.and Kashima et al.defined kernels between graphs. In 2010, Vishwanathan et al. gave their unified framework. In 2018, Ghosh et al. described the history of graph kernels and their evolution over two decades. (Wikipedia).
Graphing Squared Functions (2 of 2: Cubic + semi-circle examples)
More resources available at www.misterwootube.com
From playlist Further Work with Functions
What is a Graph? | Graph Theory
What is a graph? A graph theory graph, in particular, is the subject of discussion today. In graph theory, a graph is an ordered pair consisting of a vertex set, then an edge set. Graphs are often represented as diagrams, with dots representing vertices, and lines representing edges. Each
From playlist Graph Theory
The Definition of a Graph (Graph Theory)
The Definition of a Graph (Graph Theory) mathispower4u.com
From playlist Graph Theory (Discrete Math)
Graph Theory FAQs: 01. More General Graph Definition
In video 02: Definition of a Graph, we defined a (simple) graph as a set of vertices together with a set of edges where the edges are 2-subsets of the vertex set. Notice that this definition does not allow for multiple edges or loops. In general on this channel, we have been discussing o
From playlist Graph Theory FAQs
Graphing Equations By Plotting Points - Part 1
This video shows how to graph equations by plotting points. Part 1 of 2 http://www.mathispower4u.yolasite.com
From playlist Graphing Various Functions
Graph Theory: 02. Definition of a Graph
In this video we formally define what a graph is in Graph Theory and explain the concept with an example. In this introductory video, no previous knowledge of Graph Theory will be assumed. --An introduction to Graph Theory by Dr. Sarada Herke. This video is a remake of the "02. Definitio
From playlist Graph Theory part-1
GeoGebra Graphing Calculator
From playlist GeoGebra Graphing Calculator
What are Connected Graphs? | Graph Theory
What is a connected graph in graph theory? That is the subject of today's math lesson! A connected graph is a graph in which every pair of vertices is connected, which means there exists a path in the graph with those vertices as endpoints. We can think of it this way: if, by traveling acr
From playlist Graph Theory
Lorenzo Ruffoni - Graphical splittings of Artin kernels
38th Annual Geometric Topology Workshop (Online), June 15-17, 2021 Lorenzo Ruffoni, Florida State University Title: Graphical splittings of Artin kernels Abstract: A main feature of the theory of right-angled Artin groups (RAAGs) consists in the fact that the algebraic properties of the g
From playlist 38th Annual Geometric Topology Workshop (Online), June 15-17, 2021
What are Graph Kernels? Graph Kernels explained, Python + Graph Neural Networks
The abundance of graph-structured data and need to perform machine learning ML tasks on this data led to development of graph kernels. Machine Learning, Deep Learning. Graph kernels, this means kernel functions between graphs, have been proposed in the 2010s to solve the problem of assess
From playlist Learn Graph Neural Networks: code, examples and theory
Approximation Algs. - Lecture 19
All rights reserved for http://www.aduni.org/ Published under the Creative Commons Attribution-ShareAlike license http://creativecommons.org/licenses/by-sa/2.0/ Tutorials by Instructor: Shai Simonson. http://www.stonehill.edu/compsci/shai.htm Visit the forum at: http://www.coderisland.c
From playlist ArsDigita Algorithms by Shai Simonson
Algorithm and Hardness for Kernel Matrices in Numerical Linear Algebra...- Zhao Song
Seminar on Theoretical Machine Learning Topic: Algorithm and Hardness for Kernel Matrices in Numerical Linear Algebra and Machine Learning Speaker: Zhao Song Affiliation: Member, School of Mathematics Date: February 04, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Graph Nets: The Next Generation - Max Welling
Seminar on Theoretical Machine Learning Topic: Graph Nets: The Next Generation Speaker: Max Welling Affiliation: University of Amsterdam Date: July 21, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Kernels, marriages, and the Dinitz problem #SoME2
The Dinitz problem is a graph theory problem proposed by Jeff Dinitz in 1979, and solved by Fred Galvin in 1994, 15 years later! In the video, I share the solution, along with some motivation that could have resulted in the solution. I hope you enjoy! I first heard of the problem in Diest
From playlist Summer of Math Exposition 2 videos
Kernel Recipes 2017 - Understanding the Linux Kernel via Ftrace - Steven Rostedt
Ftrace is the official tracer of the Linux kernel. It has been apart of Linux since 2.6.31, and has grown tremendously ever since. Ftrace’s name comes from its most powerful feature: function tracing. But the ftrace infrastructure is much more than that. It also encompasses the trace event
From playlist Kernel Recipes 2017
Kaggle Live-Coding: Mapping the Data Science Package Ecosystem | Kaggle
Join Kaggle data scientist Rachael live as she works on data science projects! See all previous livestreams here: https://www.youtube.com/watch?v=JK9sOIUOysk&t=0s&list=PLqFaTIg4myu9f21aM1POYVeoaHbFf1hMc SUBSCRIBE: http://www.youtube.com/user/kaggledotcom?sub_confirmation=1&utm_medium=you
From playlist Kaggle Live Coding | Kaggle
Michal Pilipczuk: Introduction to parameterized algorithms, lecture II
The mini-course will provide a gentle introduction to the area of parameterized complexity, with a particular focus on methods connected to (integer) linear programming. We will start with basic techniques for the design of parameterized algorithms, such as branching, color coding, kerneli
From playlist Summer School on modern directions in discrete optimization
Lecture quadratic functions and it's solutions
👉 Learn the basics to understanding graphing quadratics. A quadratic equation is an equation whose highest exponent in the variable(s) is 2. To graph a quadratic equation, we make use of a table of values and the fact that the graph of a quadratic is a parabola which has an axis of symmetr
From playlist Graph a Quadratic in Standard Form | Essentials
Geometric Deep Learning II: Georg Gottwald
Machine Learning for the Working Mathematician: Week Six 31 March 2022 Georg Gottwald, Geometric Deep Learning II: Learning the Manifold Seminar series homepage (includes Zoom link): https://sites.google.com/view/mlwm-seminar-2022 Week Six part two lecture: https://youtu.be/q5gvsmF474k
From playlist Machine Learning for the Working Mathematician