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tree (5)
regress (4)

Stem transduct$ (all stems)

53 papers:

ECIRECIR-2015-RomeoIT #classification #documentation #knowledge-based #multi #representation
Knowledge-Based Representation for Transductive Multilingual Document Classification (SR, DI, AT), pp. 92–103.
ICMLICML-2015-LiuY #graph #learning #predict
Bipartite Edge Prediction via Transductive Learning over Product Graphs (HL, YY), pp. 1880–1888.
RecSysRecSys-2015-BarjastehFMER #recommendation
Cold-Start Item and User Recommendation with Decoupled Completion and Transduction (IB, RF, FM, AHE, HR), pp. 91–98.
CSLCSL-2015-CartonD #transducer
Aperiodic Two-way Transducers and FO-Transductions (OC, LD), pp. 160–174.
DLTDLT-2014-SprungerTEM #sequence
Eigenvalues and Transduction of Morphic Sequences (DS, WT, JE, LSM), pp. 239–251.
ECIRECIR-2014-LuoGWL #algorithm #classification #named #network #novel
HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks (CL, RG, ZW, CL), pp. 210–221.
ICMLICML-c2-2014-NiuDPS #approximate #learning #multi
Transductive Learning with Multi-class Volume Approximation (GN, BD, MCdP, MS), pp. 1377–1385.
ICPRICPR-2014-SousaSB #case study #classification #set
Time Series Transductive Classification on Imbalanced Data Sets: An Experimental Study (CARdS, VMAdS, GEAPAB), pp. 3780–3785.
KDDKDD-2014-Kushnir #adaptation #kernel #learning
Active-transductive learning with label-adapted kernels (DK), pp. 462–471.
KDDKDD-2014-WangNH #adaptation #induction #learning #scalability
Large-scale adaptive semi-supervised learning via unified inductive and transductive model (DW, FN, HH), pp. 482–491.
CIAACIAA-2013-ChistikovM #theorem #word
A Uniformization Theorem for Nested Word to Word Transductions (DVC, RM), pp. 97–108.
CAVCAV-2013-ClaessenFIPW #model checking #network #reachability #set
Model-Checking Signal Transduction Networks through Decreasing Reachability Sets (KC, JF, SI, NP, QW), pp. 85–100.
KDDKDD-2012-YuDRZY #classification #multi #predict
Transductive multi-label ensemble classification for protein function prediction (GXY, CD, HR, GZ, ZY), pp. 1077–1085.
MLDMMLDM-2012-CeciAVMPG #classification #paradigm #relational
Transductive Relational Classification in the Co-training Paradigm (MC, AA, HLV, DM, EP, HG), pp. 11–25.
ICMLICML-2011-Clark
Inference of Inversion Transduction Grammars (AC), pp. 201–208.
SIGIRSIGIR-2011-AminiU #automation #detection #learning #multi #summary
Transductive learning over automatically detected themes for multi-document summarization (MRA, NU), pp. 1193–1194.
CIAACIAA-2010-HundeshagenOV #automaton
Transductions Computed by PC-Systems of Monotone Deterministic Restarting Automata (NH, FO, MV), pp. 163–172.
SACSAC-2010-AppiceCM #learning
Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.
CIKMCIKM-2009-QuanzH #learning #scalability
Large margin transductive transfer learning (BQ, JH), pp. 1327–1336.
ICMLICML-2009-SindhwaniML #design #nondeterminism
Uncertainty sampling and transductive experimental design for active dual supervision (VS, PM, RDL), pp. 953–960.
SIGIRSIGIR-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.
ICMLICML-2008-CortesMPR #algorithm
Stability of transductive regression algorithms (CC, MM, DP, AR), pp. 176–183.
ICMLICML-2008-KarlenWEC #scalability
Large scale manifold transduction (MK, JW, AE, RC), pp. 448–455.
ICMLICML-2008-WangJC #graph
Graph transduction via alternating minimization (JW, TJ, SFC), pp. 1144–1151.
DLTDLT-2007-Matthissen #logic
Definable Transductions and Weighted Logics for Texts (CM), pp. 324–336.
ICMLICML-2007-LiYW #distance #framework #learning #metric #reduction
A transductive framework of distance metric learning by spectral dimensionality reduction (FL, JY, JW), pp. 513–520.
ICMLICML-2007-WangYHLT
Transductive regression piloted by inter-manifold relations (HW, SY, TSH, JL, XT), pp. 967–974.
ICMLICML-2007-ZhouB #clustering #learning #multi
Spectral clustering and transductive learning with multiple views (DZ, CJCB), pp. 1159–1166.
ICMLICML-2007-ZienBS
Transductive support vector machines for structured variables (AZ, UB, TS), pp. 1183–1190.
MLDMMLDM-2007-CeciABM #learning #relational
Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.
MLDMMLDM-2007-VanderlooyMS #empirical #evaluation #learning
Off-Line Learning with Transductive Confidence Machines: An Empirical Evaluation (SV, LvdM, IGSK), pp. 310–323.
ICMLICML-2006-Hanneke #analysis #graph #learning
An analysis of graph cut size for transductive learning (SH), pp. 393–399.
ICMLICML-2006-YuBT #design #learning
Active learning via transductive experimental design (KY, JB, VT), pp. 1081–1088.
KDDKDD-2006-BarbaraDR #detection #statistics #testing #using
Detecting outliers using transduction and statistical testing (DB, CD, JPR), pp. 55–64.
SACSAC-2006-CraigL #classification #learning #using
Protein classification using transductive learning on phylogenetic profiles (RAC, LL), pp. 161–166.
ICDARICDAR-2005-BargeronVS #detection #learning
Boosting-based Transductive Learning for Text Detection (DB, PAV, PYS), pp. 1166–1171.
ICMLICML-2005-CortesMW #learning
A general regression technique for learning transductions (CC, MM, JW), pp. 153–160.
ICMLICML-2005-SindhwaniNB #learning
Beyond the point cloud: from transductive to semi-supervised learning (VS, PN, MB), pp. 824–831.
SACSAC-2005-FaederBH #network #representation #rule-based #visual notation
Graphical rule-based representation of signal-transduction networks (JRF, MLB, WSH), pp. 133–140.
ICGTICGT-2004-Urvoy #composition
Composition of Path Transductions (TU), pp. 368–382.
ICMLICML-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).
ICMLICML-2003-Joachims #clustering #graph #learning
Transductive Learning via Spectral Graph Partitioning (TJ), pp. 290–297.
CIAACIAA-2000-Johnson #lessons learnt #specification
Lessons from INR in the Specification of Transductions (JHJ), pp. 335–336.
ICMLICML-1999-Joachims #classification #using
Transductive Inference for Text Classification using Support Vector Machines (TJ), pp. 200–209.
ICMLICML-1999-WuBCS #induction #scalability
Large Margin Trees for Induction and Transduction (DW, KPB, NC, JST), pp. 474–483.
RTARTA-1993-Raoult #recursion
Recursively Defined Tree Transductions (JCR), pp. 343–357.
ICALPICALP-1987-Johnson #finite
Single-Valued Finite Transduction (JHJ), pp. 202–211.
ICALPICALP-1986-Mascle #matrix
Torsion Matrix Semigroups and Recognizable Transductions (JPM), pp. 244–253.
ICGTGG-1982-Messerschmidt #automation #graph #natural language
Graph transductions in the field of automatic translation of natural languages (JM), pp. 255–266.
ICALPICALP-1974-Blattner #context-free grammar #set
Transductions of Context-Free Languages into Sets of Sentential Forms (MB), pp. 511–522.
STOCSTOC-1973-Baker #product line
Tree Transductions and Families of Tree Languges (BSB), pp. 200–206.
ICALPICALP-1972-HenkeIW #automaton #recursion
Hierarchies of Primitive Recursive Wordfunctions and Transductions Defined by Automata (FWvH, KI, KW), pp. 549–561.
STOCSTOC-1970-MartinV #on the #transducer
On Syntax-Directed Transduction and Tree Transducers (DFM, SAV), pp. 129–135.

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.