'Classification learning' presentation slideshows

Data Mining Chapter 2 Input: Concepts, Instances, and Attributes

Data Mining Chapter 2 Input: Concepts, Instances, and Attributes. Kirk Scott. Hopefully the idea of instances and attributes is clear Assuming there is something in the data to be mined, either this is the concept, or the concept is inherent in this

1.16k views • 92 slides

4 th Nov, 2002.

Happy Deepavali!. 10/25. 4 th Nov, 2002. Text Classification. Classification Learning (aka supervised learning). Evaluating Classification techniques: Accuracy on test data (for various sizes of training data) (if you want to separate omission/commission errors

528 views • 36 slides

Practical Machine Learning Tools and Techniques

379 views • 36 slides

Text Classification

Text Classification. Classification Learning (aka supervised learning). Given labelled examples of a concept (called training examples) Learn to predit the class label of new (unseen) examples

561 views • 31 slides

382 views • 29 slides

Data Mining CSCI 307 Spring, 2019

Data Mining CSCI 307 Spring, 2019. Lecture 4 Input , Concepts, Instances, and Attributes. Terminology. Components of the input: Concept : Thing to be learned Concept Description : Output of learning scheme Aim : intelligible and operational concept description

436 views • 23 slides