Stem rough$ (all stems)

37 papers:

SAC-2015-ChaudhuriMG #network #predict #using

QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression (AC, SM, SKG), pp. 659–661.

CASE-2014-KamarthiSZ #estimation

In-situ work piece surface roughness estimation in turning (SK, SS, AZ), pp. 328–332.

KR-2014-BeekSH #semantics #set #web

Rough Set Semantics for Identity on the Web (WB, SS, FvH).

DAC-2011-BanY #layout #modelling #optimisation

Layout aware line-edge roughness modeling and poly optimization for leakage minimization (YB, JSY), pp. 447–452.

ICEIS-v2-2011-FanLL #performance #set

Influencing Factors of High-speed Railway Passengers’ Travel Choice based on Rough Set (YF, JL, CL), pp. 213–217.

ICEIS-v4-2011-FengLL #evaluation #set

Comprehensive Evaluation of the Railway Passenger’s Satisfaction based on Rough Set and Entropy (YqF, XwL, XmL), pp. 629–635.

CIKM-2010-ZhouLBXL #information management #mining #reasoning #set

Rough sets based reasoning and pattern mining for a two-stage information filtering system (XZ, YL, PB, YX, RYKL), pp. 1429–1432.

ICPR-2010-SeppkeDHW #equation #multi #parametricity #performance #using

Fast Derivation of Soil Surface Roughness Parameters Using Multi-band SAR Imagery and the Integral Equation Model (BS, LSDF, JAH, FW), pp. 3931–3934.

KDIR-2010-ThanhYU #clustering #documentation #scalability #set #similarity

Clustering Documents with Large Overlap of Terms into Different Clusters based on Similarity Rough Set Model (NCT, KY, MU), pp. 396–399.

SAC-2010-Kaneiwa #approach #information management #mining #set

A rough set approach to mining connections from information systems (KK), pp. 990–996.

DATE-2009-ChenW #3d #modelling #simulation

New simulation methodology of 3D surface roughness loss for interconnects modeling (QC, NW), pp. 1184–1189.

DAC-2008-YeLNC #modelling #simulation #statistics

Statistical modeling and simulation of threshold variation under dopant fluctuations and line-edge roughness (YY, FL, SRN, YC), pp. 900–905.

HPCA-2008-LeeB #architecture #design #optimisation

Roughness of microarchitectural design topologies and its implications for optimization (BCL, DMB), pp. 240–251.

HIMI-MTT-2007-IshizuGNI #ontology #set

Rough Ontology: Extension of Ontologies by Rough Sets (SI, AG, YN, YI), pp. 456–462.

ICEIS-AIDSS-2006-Grzeszczyk #evaluation #set

Application of the Rough Set Method for Evaluation of Structural Funds Projects (TAG), pp. 202–207.

ICPR-v1-2006-RaghebH

Reflectance from Surfaces with Layers of Variable Roughness (HR, ERH), pp. 543–546.

ICEIS-v2-2005-KumarKDB #clustering #mining #using #web

Web Usage Mining Using Rough Agglomerative Clustering (PK, PRK, SKD, RSB), pp. 315–320.

SIGMOD-2004-CormodeKMS #multi

Diamond in the Rough: Finding Hierarchical Heavy Hitters in Multi-Dimensional Data (GC, FK, SM, DS), pp. 155–166.

ICEIS-v2-2004-WuW04a #algorithm #comparison #set

Result Comparison of Two Rough Set Based Discretization Algorithms (SW, WW), pp. 511–514.

ICPR-v3-2004-FurukawaKMSMT #3d #approximate #estimation #image #physics #using

Spine Posture Estimation Method from Human Images Using 3D Spine Model — Computation of the rough approximation of the physical forces working on vertebral bodies (DF, TK, KM, YS, KM, TT), pp. 322–325.

SEKE-2004-JahnkeB #architecture #clustering #reverse engineering #using

Reverse Engineering Software Architecture using Rough Clusters (JHJ, YB), pp. 270–275.

ICEIS-v2-2003-HassanienA #algorithm #classification #image #performance #retrieval #set

An Efficient Classification and Image Retrieval Algorithm Based on Rough Set Theory (AEH, JMHA), pp. 457–460.

ICPR-v2-2000-StraussCA #robust #statistics

Rough Histograms for Robust Statistics (OS, FC, MJA), pp. 2684–2687.

ICPR-v3-2000-MaedaINTS #algorithm #fuzzy #image #segmentation #using

Rough and Accurate Segmentation of Natural Color Images Using Fuzzy Region-Growing Algorithm (JM, CI, SN, NT, YS), pp. 3642–3645.

ICPR-v3-2000-PetrosinoC #clustering #fuzzy #parallel #set

Unsupervised Texture Discrimination Based on Rough Fuzzy Sets and Parallel Hierarchical Clustering (AP, MC), pp. 7100–7103.

SAC-1998-MachucaM #data mining #database #mining #precise #relational #set

Enhancing the exploitation of data mining in relational database systems via the rough sets theory including precision variables (FM, MM), pp. 70–73.

ICDAR-1997-WaizumiKSN #classification #learning #using

High speed rough classification for handwritten characters using hierarchical learning vector quantization (YW, NK, KS, YN), pp. 23–27.

KDD-1996-ShanZHC #classification #database #set #using

Discovering Classification Knowledge in Databases Using Rough Sets (NS, WZ, HJH, NC), pp. 271–274.

KDD-1996-TsumotoT #automation #database #set

Automated Discovery of Medical Expert System Rules from Clinical Databases Based on Rough Sets (ST, HT), pp. 63–69.

SAC-1996-HasemiPAHP #approach #classification #search-based

A rough-genetic approach for classification of complex data (RH, BAP, RBA, WGH, MGP), pp. 282–288.

KDD-1995-DeogunRS #approximate #set

Exploiting Upper Approximation in the Rough Set Methodology (JSD, VVR, HS), pp. 69–74.

KDD-1995-HuC #database #learning #set #similarity

Rough Sets Similarity-Based Learning from Databases (XH, NC), pp. 162–167.

KDD-1995-ShanZHC #information management #set #tool support #using

Using Rough Sets as Tools for Knowledge Discovery (NS, WZ, HJH, NC), pp. 263–268.

KDD-1995-SkowronS #approach #concurrent #modelling #set

Discovery of Concurrent Data Models from Experimental Tables: A Rough Set Approach (AS, ZS), pp. 288–293.

KDD-1995-TsumotoT95a #automation #component #functional #representation #sequence #set

Automated Discovery of Functional Components of Proteins from Amino-Acid Sequences Based on Rough Sets and Change of Representation (ST, HT), pp. 318–324.

CIKM-1994-HuC #approach #database #relational #set

Discovery of Decision Rules in Relational Databases: A Rough Set Approach (XH, NC), pp. 392–400.

SIGIR-1988-Das-Gupta #information retrieval #set

Rough Sets and Information Retrieval (PDG), pp. 567–581.