Skip to main navigation
Skip to search
Skip to main content
Illinois Experts Home
LOGIN & Help
Home
Profiles
Research units
Research & Scholarship
Datasets
Honors
Press/Media
Activities
Search by expertise, name or affiliation
Universal piecewise linear prediction via context trees
Suleyman S. Kozat
,
Andrew C. Singer
, Georg Christoph Zeitler
Electrical and Computer Engineering
Coordinated Science Lab
Industrial and Enterprise Systems Engineering
Beckman Institute for Advanced Science and Technology
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Universal piecewise linear prediction via context trees'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Affine Model
25%
Algorithm Approach
25%
Best Linear Predictor
25%
Competitive Algorithm
25%
Context Tree
100%
Doubly Exponential
25%
Entire Sequence
50%
Error Performance
25%
Exponential number
25%
High-order
25%
Individual Sequences
25%
Linear Prediction
100%
Linear Predictor
50%
Partitioning Method
25%
Piecewise Linear
100%
Piecewise Linear Model
25%
Prediction Algorithms
50%
Prediction Parameters
25%
Regressor
25%
Regret
50%
Scalar
50%
Squared Prediction Error
50%
Computer Science
Algorithmic Description
20%
Entire Sequence
40%
Exponential Number
20%
Linear Predictor
60%
piecewise linear
100%
Prediction Error
40%