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163
Voted
TIP
2010
182views more  TIP 2010»
14 years 8 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
FOCS
2005
IEEE
15 years 7 months ago
Lower Bounds for the Noisy Broadcast Problem
We prove the first non-trivial (super linear) lower bound in the noisy broadcast model, defined by El Gamal in [6]. In this model there are n + 1 processors P0, P1, . . . , Pn, ...
Navin Goyal, Guy Kindler, Michael E. Saks
PPSN
1990
Springer
15 years 6 months ago
Feature Construction for Back-Propagation
T h e ease of learning concepts f r o m examples in empirical machine learning depends on the attributes used for describing the training d a t a . We show t h a t decision-tree b...
Selwyn Piramuthu
105
Voted
ICIP
1995
IEEE
16 years 3 months ago
Constrained monotone regression of ROC curves and histograms using splines and polynomials
Receiver operating characteristics (ROC) curves have the property that they start at (0,l) and end at (1,O) and are monotonically decreasing. Furthermore, a parametric representat...
Tapas Kanungo, D. M. Gay, Robert M. Haralick
DAC
2009
ACM
16 years 2 months ago
Way Stealing:cache-assisted automatic instruction set extensions
This paper introduces Way Stealing, a simple architectural modification to a cache-based processor to increase data bandwidth to and from application-specific Instruction Set Exte...
Theo Kluter, Philip Brisk, Paolo Ienne, Edoardo Ch...
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