## 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.