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COLT
1999
Springer
13 years 12 months ago
Multiclass Learning, Boosting, and Error-Correcting Codes
We focus on methods to solve multiclass learning problems by using only simple and efficient binary learners. We investigate the approach of Dietterich and Bakiri [2] based on er...
Venkatesan Guruswami, Amit Sahai
IRI
2007
IEEE
14 years 1 months ago
Adapting Ratings in Memory-Based Collaborative Filtering using Linear Regression
We show that the standard memory-based collaborative filtering rating prediction algorithm using the Pearson correlation can be improved by adapting user ratings using linear reg...
Jérôme Kunegis, Sahin Albayrak
CDC
2009
IEEE
129views Control Systems» more  CDC 2009»
13 years 11 months ago
Improving the performance of active set based Model Predictive Controls by dataflow methods
Abstract-- Dataflow representations of Digital Signal Processing (DSP) software have been developing since the 1980's. They have proven to be useful in identifying bottlenecks...
Ruirui Gu, Shuvra S. Bhattacharyya, William S. Lev...
WOSP
2010
ACM
13 years 7 months ago
Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms
Quantitative prediction of quality properties (i.e. extrafunctional properties such as performance, reliability, and cost) of software architectures during design supports a syste...
Anne Martens, Heiko Koziolek, Steffen Becker, Ralf...
CIKM
2008
Springer
13 years 9 months ago
Group-based learning: a boosting approach
This paper points out that many machine learning problems in IR should be and can be formalized in a novel way, referred to as `group-based learning'. In group-based learning...
Weijian Ni, Jun Xu, Hang Li, Yalou Huang