'classification' Category
POTW 6/24/07: “Support-Vector Networks” by Cortes and Vapnik
Long paper this week, but it is the original on Support Vector Machines: Support-Vector Networks by Cortes and Vapnik. Given my schedule, I may spread this out over two weeks.
Popularity: 21% [?]Popularity: 21% [?]
POTW 6/11/07: Discussion of “A Sequential Algorithm for Training Text Classifiers” by Lewis and Gale
In “A Sequential Algorithm for Training Text Classifiers” by David D.
Lewis and William Gale, the authors put forth a new (at the time)
method training text classifiers using an approach they call
“uncertainty sampling”
Section 1 outlines the problem of training, namely obtaining a good
sample of text to be labeled for the trainer. After disposing of
several other methods [...]Popularity: 21% [?]
POTW 6/11/07: “A Sequential Algorithm for Training Text Classifiers” by Lewis and Gale
More on text classification: “A Sequential Algorithm for Training Text Classifiers” by David Lewis and William Gale. A little bit of an older paper, but still looks to be a good one.
Popularity: 21% [?]Popularity: 21% [?]
POTW 6/3/07: Discussion of “A Comparison of Event Models for Naive Bayes Text Classification” by Andrew McCallum and Kamal Nigam
We are reading “A Comparison of Event Models for Naive Bayes Text Classification” by McCallum and Nigam.
Text classification is the process of assigning a document to one or more categories (we looked at classification/categorization earlier when exploring Support Vector Machines, SVMs). My understanding of the difference between categorization and classification is that categorization has a [...]Popularity: 14% [?]
POTW 6/3/07: “A Comparison of Event Models for Naive Bayes Text Classification” by Andrew McCallum and Kamal Nigam
Paper of the week for the week of June 3, 2007 is “A Comparison of event Models for Naive Bayes Text Classification” by Andrew McCallum and Kamal Nigam. This paper promises to shed some light on different ways of using bayesian classifiers. It might be useful to do some background reading on naive [...]
Popularity: 12% [?]
