## Stem condens$ (all stems)

### 35 papers:

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