Stem transduct$ (all stems)

53 papers:

ECIR-2015-RomeoIT #classification #documentation #knowledge-based #multi #representation

Knowledge-Based Representation for Transductive Multilingual Document Classification (SR, DI, AT), pp. 92–103.

ICML-2015-LiuY #graph #learning #predict

Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.

RecSys-2015-BarjastehFMER #recommendation

Cold-Start Item and User Recommendation with Decoupled Completion and Transduction (IB, RF, FM, AHE, HR), pp. 91–98.

CSL-2015-CartonD #transducer

Aperiodic Two-way Transducers and FO-Transductions (OC, LD), pp. 160–174.

DLT-2014-SprungerTEM #sequence

Eigenvalues and Transduction of Morphic Sequences (DS, WT, JE, LSM), pp. 239–251.

ECIR-2014-LuoGWL #algorithm #classification #named #network #novel

HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks (CL, RG, ZW, CL), pp. 210–221.

ICML-c2-2014-NiuDPS #approximate #learning #multi

Transductive Learning with Multi-class Volume Approximation (GN, BD, MCdP, MS), pp. 1377–1385.

ICPR-2014-SousaSB #case study #classification #set

Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study (CARdS, VMAdS, GEAPAB), pp. 3780–3785.

KDD-2014-Kushnir #adaptation #kernel #learning

Active-transductive learning with label-adapted kernels (DK), pp. 462–471.

KDD-2014-WangNH #adaptation #induction #learning #scalability

Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.

CIAA-2013-ChistikovM #theorem #word

A Uniformization Theorem for Nested Word to Word Transductions (DVC, RM), pp. 97–108.

CAV-2013-ClaessenFIPW #model checking #network #reachability #set

Model-Checking Signal Transduction Networks through Decreasing Reachability Sets (KC, JF, SI, NP, QW), pp. 85–100.

KDD-2012-YuDRZY #classification #multi #predict

Transductive multi-label ensemble classification for protein function prediction (GXY, CD, HR, GZ, ZY), pp. 1077–1085.

MLDM-2012-CeciAVMPG #classification #paradigm #relational

Transductive Relational Classification in the Co-training Paradigm (MC, AA, HLV, DM, EP, HG), pp. 11–25.

ICML-2011-Clark

Inference of Inversion Transduction Grammars (AC), pp. 201–208.

SIGIR-2011-AminiU #automation #detection #learning #multi #summary

Transductive learning over automatically detected themes for multi-document summarization (MRA, NU), pp. 1193–1194.

CIAA-2010-HundeshagenOV #automaton

Transductions Computed by PC-Systems of Monotone Deterministic Restarting Automata (NH, FO, MV), pp. 163–172.

SAC-2010-AppiceCM #learning

Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.

CIKM-2009-QuanzH #learning #scalability

Large margin transductive transfer learning (BQ, JH), pp. 1327–1336.

ICML-2009-SindhwaniML #design #nondeterminism

Uncertainty sampling and transductive experimental design for active dual supervision (VS, PM, RDL), pp. 953–960.

SIGIR-2009-AminiU #algorithm #information management #multi #ranking #summary

Incorporating prior knowledge into a transductive ranking algorithm for multi-document summarization (MRA, NU), pp. 704–705.

ICML-2008-CortesMPR #algorithm

Stability of transductive regression algorithms (CC, MM, DP, AR), pp. 176–183.

ICML-2008-KarlenWEC #scalability

Large scale manifold transduction (MK, JW, AE, RC), pp. 448–455.

ICML-2008-WangJC #graph

Graph transduction via alternating minimization (JW, TJ, SFC), pp. 1144–1151.

DLT-2007-Matthissen #logic

Definable Transductions and Weighted Logics for Texts (CM), pp. 324–336.

ICML-2007-LiYW #distance #framework #learning #metric #reduction

A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.

ICML-2007-WangYHLT

Transductive regression piloted by inter-manifold relations (HW, SY, TSH, JL, XT), pp. 967–974.

ICML-2007-ZhouB #clustering #learning #multi

Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.

ICML-2007-ZienBS

Transductive support vector machines for structured variables (AZ, UB, TS), pp. 1183–1190.

MLDM-2007-CeciABM #learning #relational

Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.

MLDM-2007-VanderlooyMS #empirical #evaluation #learning

Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation (SV, LvdM, IGSK), pp. 310–323.

ICML-2006-Hanneke #analysis #graph #learning

An analysis of graph cut size for transductive learning (SH), pp. 393–399.

ICML-2006-YuBT #design #learning

Active learning via transductive experimental design (KY, JB, VT), pp. 1081–1088.

KDD-2006-BarbaraDR #detection #statistics #testing #using

Detecting outliers using transduction and statistical testing (DB, CD, JPR), pp. 55–64.

SAC-2006-CraigL #classification #learning #using

Protein classification using transductive learning on phylogenetic profiles (RAC, LL), pp. 161–166.

ICDAR-2005-BargeronVS #detection #learning

Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.

ICML-2005-CortesMW #learning

A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.

ICML-2005-SindhwaniNB #learning

Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.

SAC-2005-FaederBH #network #representation #rule-based #visual notation

Graphical rule-based representation of signal-transduction networks (JRF, MLB, WSH), pp. 133–140.

ICGT-2004-Urvoy #composition

Composition of Path Transductions (TU), pp. 368–382.

ICML-2004-ZhangYK #algorithm #kernel #learning #matrix #using

Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm (ZZ, DYY, JTK).

ICML-2003-Joachims #clustering #graph #learning

Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.

CIAA-2000-Johnson #lessons learnt #specification

Lessons from INR in the Specification of Transductions (JHJ), pp. 335–336.

ICML-1999-Joachims #classification #using

Transductive Inference for Text Classification using Support Vector Machines (TJ), pp. 200–209.

ICML-1999-WuBCS #induction #scalability

Large Margin Trees for Induction and Transduction (DW, KPB, NC, JST), pp. 474–483.

RTA-1993-Raoult #recursion

Recursively Defined Tree Transductions (JCR), pp. 343–357.

ICALP-1987-Johnson #finite

Single-Valued Finite Transduction (JHJ), pp. 202–211.

ICALP-1986-Mascle #matrix

Torsion Matrix Semigroups and Recognizable Transductions (JPM), pp. 244–253.

GG-1982-Messerschmidt #automation #graph #natural language

Graph transductions in the field of automatic translation of natural languages (JM), pp. 255–266.

ICALP-1974-Blattner #context-free grammar #set

Transductions of Context-Free Languages into Sets of Sentential Forms (MB), pp. 511–522.

STOC-1973-Baker #product line

Tree Transductions and Families of Tree Languges (BSB), pp. 200–206.

ICALP-1972-HenkeIW #automaton #recursion

Hierarchies of Primitive Recursive Wordfunctions and Transductions Defined by Automata (FWvH, KI, KW), pp. 549–561.

STOC-1970-MartinV #on the #transducer

On Syntax-Directed Transduction and Tree Transducers (DFM, SAV), pp. 129–135.