BibSLEIGH
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algorithm (5)
use (5)
nearest (4)
represent (4)
track (4)

Stem condens$ (all stems)

35 papers:

ICALPICALP-v1-2015-SkorskiGP #predict
Condensed Unpredictability (MS, AG, KP), pp. 1046–1057.
ICPCICPC-2014-ThungLOC #classification #design #diagrams #metric #network #using
Condensing class diagrams by analyzing design and network metrics using optimistic classification (FT, DL, MHO, MRVC), pp. 110–121.
ICMLICML-c1-2014-LiL #classification #multi
Condensed Filter Tree for Cost-Sensitive Multi-Label Classification (CLL, HTL), pp. 423–431.
ICSMEICSM-2013-OsmanCP #algorithm #analysis #diagrams #machine learning
An Analysis of Machine Learning Algorithms for Condensing Reverse Engineered Class Diagrams (MHO, MRVC, PvdP), pp. 140–149.
PLDIPLDI-2013-JohnsonOZA #dependence #graph #performance
Fast condensation of the program dependence graph (NPJ, TO, AZ, DIA), pp. 39–50.
SIGIRSIGIR-2013-BonzaniniMR #summary
Extractive summarisation via sentence removal: condensing relevant sentences into a short summary (MB, MMA, TR), pp. 893–896.
STOCSTOC-2012-Li #design #privacy
Design extractors, non-malleable condensers and privacy amplification (XL), pp. 837–854.
ICPRICPR-2008-KrizekKH #algorithm #feature model
Feature condensing algorithm for feature selection (PK, JK, VH), pp. 1–4.
KDDKDD-2008-CaroCS #using
Using tagflake for condensing navigable tag hierarchies from tag clouds (LDC, KSC, MLS), pp. 1069–1072.
ICPRICPR-v1-2006-YalcinG #algorithm #difference #evolution
Integrating Differential Evolution and Condensation Algorithms for License Plate Tracking (IKY, MG), pp. 658–661.
ICPRICPR-v2-2006-ChouKC #nearest neighbour #reduction
The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method (CHC, BHK, FC), pp. 556–559.
STOCSTOC-2005-BarakKSSW #graph #independence #simulation
Simulating independence: new constructions of condensers, ramsey graphs, dispersers, and extractors (BB, GK, RS, BS, AW), pp. 1–10.
ICMLICML-2005-Angiulli #nearest neighbour #performance
Fast condensed nearest neighbor rule (FA), pp. 25–32.
CSMRCSMR-2004-DucasseLB #runtime
High-Level Polymetric Views of Condensed Run-time Information (SD, ML, RB), pp. 309–318.
ICPRICPR-v3-2004-KatoW #algorithm #classification #nearest neighbour #performance
Direct Condensing: An Efficient Voronoi Condensing Algorithm for Nearest Neighbor Classifiers (TK, TW), pp. 474–477.
ICPRICPR-v3-2004-SatakeS #multi #using
Multiple Target Tracking by Appearance-Based Condensation Tracker using Structure Information (JS, TS), pp. 294–297.
ICPRICPR-v4-2004-FrenchMP
Condensation Tracking through a Hough Space (APF, SM, TPP), pp. 195–198.
KRKR-2004-RaedtR #induction #logic programming
Condensed Representations for Inductive Logic Programming (LDR, JR), pp. 438–446.
ICSTSAT-2003-HanataniHI
Density Condensation of Boolean Formulas (YH, TH, KI), pp. 69–77.
ICPRICPR-v1-2002-KangKB #multi #people #realtime #using
Real-Time Multiple People Tracking Using Competitive Condensation (HK, DK, SYB), pp. 413–416.
PODSPODS-2001-BykowskiR #representation
A condensed representation to find frequent patterns (AB, CR).
STOCSTOC-2001-Ta-ShmaUZ
Loss-less condensers, unbalanced expanders, and extractors (ATS, CU, DZ), pp. 143–152.
ICPRICPR-v2-2000-DasarathyS #algorithm #editing #nearest neighbour
Tandem Fusion of Nearest Neighbor Editing and Condensing Algorithms — Data Dimensionality Effects (BVD, JSS), pp. 2692–2695.
ICPRICPR-v2-2000-MitraMP #database #incremental #learning #scalability
Data Condensation in Large Databases by Incremental Learning with Support Vector Machines (PM, CAM, SKP), pp. 2708–2711.
SIGIRSIGIR-1999-WitbrockM #approach #generative #named #statistics #summary
Ultra-Summarization: A Statistical Approach to Generating Highly Condensed Non-Extractive Summaries (poster abstract) (MJW, VOM), pp. 315–316.
CIAAWIA-1998-Goeman #linear #lr #on the #parsing #string
On Parsing and Condensing Substrings of LR Languages in Linear Time (HG), pp. 22–42.
IFLIFL-1997-Scholz #array
WITH-Loop-Folding in SAC — Condensing Consecutive Array Operations (SBS), pp. 72–91.
CADECADE-1997-FuchsF #named #problem #proving
CODE: A Powerful Prover for Problems of Condensed Detachment (DF, MF), pp. 260–263.
ICLPILPS-1997-Mannila #data mining #database #induction #mining
Inductive Databases and Condensed Representations for Data Mining (HM), pp. 21–30.
KDDKDD-1996-Langley #induction
Induction of Condensed Determinations (PL), pp. 327–330.
KDDKDD-1996-MannilaT96a #multi #set
Multiple Uses of Frequent Sets and Condensed Representations (Extended Abstract) (HM, HT), pp. 189–194.
CADECADE-1992-McCuneW #automation #deduction
Experiments in Automated Deduction with Condensed Detachment (WM, LW), pp. 209–223.
DACDAC-1989-Blanks #clustering #probability
Partitioning by Probability Condensation (JB), pp. 758–761.
RTARTA-1989-Lankford #theory and practice
Generalized Gröbner Bases: Theory and Applications. A Condensation (DL), pp. 203–221.
CADECADE-1986-OppacherS #deduction #heuristic #proving
Controlling Deduction with Proof Condensation and Heuristics (FO, ES), pp. 384–393.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
Hosted as a part of SLEBOK on GitHub.