Quasi-Monotonic Segmentation Talk in Ottawa

I’m giving a talk next week at the Text Analysis and Machine Learning Group (TAMALE) seminar at the University of Ottawa. I will talk on Optimal Linear Time Algorithm for Quasi-Monotonic Segmentation. It is not directly related to text and machine learning, but many of the ideas from time series data mining port over to text processing. After all, a sequence is a sequence. I see Joel Martin wil also give a talk there this Spring on “Libminer”. Here’s the abstract for my talk:

Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting an array in up to K segments. We want segments to be as monotonic as possible and to alternate signs. We propose a quality metric for this problem, present an optimal linear time algorithm based on novel formalism, and compare experimentally its performance to a linear time top-down regression algorithm. We show that our algorithm is faster and more accurate. Applications include pattern recognition and qualitative modeling.

Published by

Daniel Lemire

A computer science professor at the University of Quebec (TELUQ).

Leave a Reply

Your email address will not be published. The comment form expects plain text. If you need to format your text, you can use HTML elements such strong, blockquote, cite, code and em. For formatting code as HTML automatically, I recommend tohtml.com.

You may subscribe to this blog by email.