Lifelong Machine Learning and Computer Reading the Web

author: Bing Liu, University of Illinois at Urbana-Champaign
author: Estevam R. Hruschka, Federal University of Săo Carlos
author: Zhiyuan (Brett) Chen, Department of Computer Science, University of Illinois at Chicago
published: Sept. 9, 2016,   recorded: August 2016,   views: 2431
Categories

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 29:19
!NOW PLAYING
Watch Part 2
Part 2 1:01:10
!NOW PLAYING
Watch Part 3
Part 3 40:54
!NOW PLAYING

Description

This tutorial introduces Lifelong Machine Learning (LML) and Machine Reading. The core idea of LML is to learn continuously and accumulate the learned knowledge, and to use the knowledge to help future learning, which is perhaps the hallmark of human learning and human intelligence. By us- ing prior knowledge seamlessly and effortlessly, we humans can learn without a lot of training data, but current machine learning algorithms tend to need a huge amount of training data. LML aims to mimic this human capability. Machine Reading is a research area with the goal of building systems to read natural language text. Among different approaches employed in Machine Reading, this tutorial focuses on projects and approaches that use the idea of LML. Most current machine learning (ML) algorithms learn in isolation. They are designed to address a specific problem using a single dataset. That is, given a dataset, an ML algorithm is executed on the dataset to build a model. Although this type of isolated learning is very useful, it does not have the ability to accumulate past knowledge and to make use of the knowledge for future learning, which we believe are critical for the future of machine learning and data mining. LML aims to design and develop computational systems and algorithms with this capability, i.e., to learn as humans do in a lifelong manner.

In this tutorial, we introduce this important problem and the existing LML techniques and discuss opportunities and challenges of big data for lifelong machine learning. We also want to motivate researchers and practitioners to actively explore LML as the big data provides us a golden opportunity to learn a large volume of diverse knowledge, to connect different pieces of it, and to use it to raise data mining and machine learning to a new level.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: