In network theory, collective classification is the simultaneous prediction of the labels for multiple objects, where each label is predicted using information about the object's observed features, the observed features and labels of its neighbors, and the unobserved labels of its neighbors. Collective classification problems are defined in terms of networks of random variables, where the network structure determines the relationship between the random variables. Inference is performed on multiple random variables simultaneously, typically by propagating information between nodes in the network to perform approximate inference. Approaches that use collective classification can make use of relational information when performing inference. Examples of collective classification include predicting attributes (ex. gender, age, political affiliation) of individuals in a social network, classifying webpages in the World Wide Web, and inferring the research area of a paper in a scientific publication dataset. (Wikipedia).
Definition of a group Lesson 24
In this video we take our first look at the definition of a group. It is basically a set of elements and the operation defined on them. If this set of elements and the operation defined on them obey the properties of closure and associativity, and if one of the elements is the identity el
From playlist Abstract algebra
Definition of a Group and Examples of Groups
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Definition of a Group and Examples of Groups
From playlist Abstract Algebra
Why Is A Group Of Crows Called A “Murder”?
Buy our clowder of cats T-shirt! https://store.dftba.com/products/cat-cat-shirt AND SUPPORT US ON PATREON: https://www.patreon.com/MinuteEarth Collective nouns are a great way to have fun with language and nature. Thank you! ___________________________________________ Collective Noun:
From playlist Society, Culture & Technology
What is a Group? | Abstract Algebra
Welcome to group theory! In today's lesson we'll be going over the definition of a group. We'll see the four group axioms in action with some examples, and some non-examples as well which violate the axioms and are thus not groups. In a fundamental way, groups are structures built from s
From playlist Abstract Algebra
Visual Group Theory, Lecture 1.6: The formal definition of a group
Visual Group Theory, Lecture 1.6: The formal definition of a group At last, after five lectures of building up our intuition of groups and numerous examples, we are ready to present the formal definition of a group. We conclude by proving several basic properties that are not built into t
From playlist Visual Group Theory
Percentiles, Deciles, Quartiles
Understanding percentiles, quartiles, and deciles through definitions and examples
From playlist Unit 1: Descriptive Statistics
Visual Group Theory, Lecture 1.4: Group presentations
Visual Group Theory, Lecture 1.4: Group presentations We begin this lecture by learning how to take a Cayley diagram and label its nodes with the elements of a group. Such a labeled diagram can function as a "group calculator". It leads to the notion of a "group presentation", which is a
From playlist Visual Group Theory
What is a Group Action? : A Group as a Category and The Skeleton Operation ☠
This week I try to take a more Categorical approach to answering and expanding upon the question of "what is a group action". Along the way I'll go over thinking about a group as a category and eventually hit on the skeleton operation on a category and use it to present an example of the c
From playlist The New CHALKboard
More Standard Deviation and Variance
Further explanations and examples of standard deviation and variance
From playlist Unit 1: Descriptive Statistics
Jutta Rogal - Machine learning collective variables for sampling of structural transformations
Recorded 26 January 2023. Jutta Rogal of New York University presents "Pathways in classification space - Machine learning collective variables for enhanced sampling of structural transformations" at IPAM's Learning and Emergence in Molecular Systems Workshop. Abstract: Microscopic process
From playlist 2023 Learning and Emergence in Molecular Systems
AdaBoost is one of those machine learning methods that seems so much more confusing than it really is. It's really just a simple twist on decision trees and random forests. NOTE: This video assumes you already know about Decision Trees... https://youtu.be/_L39rN6gz7Y ...and Random Forests
From playlist StatQuest
Samory Kpotufe: "Some New Insights On Transfer Learning"
Machine Learning for Physics and the Physics of Learning 2019 Workshop III: Validation and Guarantees in Learning Physical Models: from Patterns to Governing Equations to Laws of Nature "Some New Insights On Transfer Learning" Samory Kpotufe - Columbia University, Statistics Abstract: Th
From playlist Machine Learning for Physics and the Physics of Learning 2019
ml5: Neural Network Regression
In this video I cover how to train a neural network to perform a "regression" task (rather than classification). The result is a continuous numerical output (frequency value) instead of a categorical one (specific note). #machinelearning #ml5js #p5js 💻Code: https://thecodingtrain.com/Cour
From playlist Beginners Guide to Machine Learning in JavaScript
A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention
Clinical coding is the task of assigning a set of alphanumeric codes, referred to as ICD, to a medical event based on the context captured in a clinical narrative. The latest version of ICD, ICD-10, includes more than 70,000 codes. This talk will discuss a novel approach for automatic ICD
From playlist Healthcare NLP Summit 2022
Working with Synthetic Data | Deep Learning for Engineers, Part 2
This video covers the first step in deep learning: having access to data. Part of making the decision of whether deep learning is right for your project comes down to the type and amount of data that you have access to. We’ll cover the importance of data using the practical engineering ex
From playlist Deep Learning for Engineers
Photogrammar: A Yale NEH DH Start-Up Grant Project
15 minute presentation: preview of an interactive visualization platform for 160,000 photographs created by the federal government from 1935-1943.
From playlist New Directions for Digital Scholarship
NLP Intro For Text - Sentiment Analysis With Deep Learning - Course Overview
Welcome to this deeplizard course, NLP Intro for Text - Sentiment Analysis with Deep Learning! This course provides an introduction to the field of Natural Language Processing (NLP) with a focus on sentiment analysis and text classification using artificial neural networks. 🔥 EARLY ACCESS
From playlist NLP Intro For Text - Sentiment Analysis With Deep Learning
From playlist Belong: What It's Like to Live in the Hyphen
Bolei Zhou - From Network Dissection to Policy Dissection: Emergent Concepts in Deep Representations
Recorded 9 January 2023. Bolei Zhou of the University of California, Los Angeles, presents "From Network Dissection to Policy Dissection: Discovering Emergent Concepts in Deep Representations" at IPAM's Explainable AI for the Sciences: Towards Novel Insights Workshop. Abstract: When the d
From playlist 2023 Explainable AI for the Sciences: Towards Novel Insights