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.