--1 CONTENTS

[1]  Qiang Yang,et al.  Semi-Supervised Learning with Very Few Labeled Training Examples , 2007, AAAI.

[2]  Rong Jin,et al.  Semi-Supervised Learning by Mixed Label Propagation , 2007, AAAI.

[3]  Gholamreza Haffari,et al.  Analysis of Semi-Supervised Learning with the Yarowsky Algorithm , 2007, UAI.

[4]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[5]  Zhi-Hua Zhou,et al.  On the relation between multi-instance learning and semi-supervised learning , 2007, ICML '07.

[6]  Tong Zhang,et al.  Two-view feature generation model for semi-supervised learning , 2007, ICML '07.

[7]  Gideon S. Mann,et al.  Simple, robust, scalable semi-supervised learning via expectation regularization , 2007, ICML '07.

[8]  Stephen J. Wright,et al.  Dissimilarity in Graph-Based Semi-Supervised Classification , 2007, AISTATS.

[9]  Bernhard Schölkopf,et al.  Transductive Classification via Local Learning Regularization , 2007, AISTATS.

[10]  Julia L. Evans,et al.  Can Infants Map Meaning to Newly Segmented Words? , 2007, Psychological science.

[11]  Lorenzo Rosasco,et al.  Manifold Regularization , 2007 .

[12]  Zhi-Hua Zhou,et al.  Semi-Supervised Regression with Co-Training Style Algorithms , 2007 .

[13]  Sarah Zelikovitz,et al.  Improving Text Classification with LSI Using Background Knowledge , 2007 .

[14]  Ivor W. Tsang,et al.  Large-Scale Sparsified Manifold Regularization , 2006, NIPS.

[15]  S. Sathiya Keerthi,et al.  Branch and Bound for Semi-Supervised Support Vector Machines , 2006, NIPS.

[16]  Mikhail Belkin,et al.  On the Relation Between Low Density Separation, Spectral Clustering and Graph Cuts , 2006, NIPS.

[17]  Wei Chu,et al.  Relational Learning with Gaussian Processes , 2006, NIPS.

[18]  Bernhard Schölkopf,et al.  Learning with Hypergraphs: Clustering, Classification, and Embedding , 2006, NIPS.

[19]  Matthias Hein,et al.  Manifold Denoising , 2006, NIPS.

[20]  Dale Schuurmans,et al.  Learning to Model Spatial Dependency: Semi-Supervised Discriminative Random Fields , 2006, NIPS.

[21]  Mehryar Mohri,et al.  On Transductive Regression , 2006, NIPS.

[22]  Xinhua Zhang,et al.  Hyperparameter Learning for Graph Based Semi-supervised Learning Algorithms , 2006, NIPS.

[23]  Nello Cristianini,et al.  Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problem , 2006, J. Mach. Learn. Res..

[24]  Alexander Zien,et al.  Semi-Supervised Learning , 2006 .

[25]  Pawan Sinha,et al.  Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.

[26]  S. Sathiya Keerthi,et al.  Deterministic annealing for semi-supervised kernel machines , 2006, ICML.

[27]  Alexander Zien,et al.  A continuation method for semi-supervised SVMs , 2006, ICML.

[28]  Jason Weston,et al.  Inference with the Universum , 2006, ICML.

[29]  Ulf Brefeld,et al.  Semi-supervised learning for structured output variables , 2006, ICML.

[30]  Jason Weston,et al.  Trading convexity for scalability , 2006, ICML.

[31]  Xin Yang,et al.  Semi-supervised nonlinear dimensionality reduction , 2006, ICML.

[32]  Xiaojin Zhu,et al.  Seeing stars when there aren’t many stars: Graph-based semi-supervised learning for sentiment categorization , 2006 .

[33]  Eytan Domany,et al.  Semi-Supervised Learning -- A Statistical Physics Approach , 2006, ArXiv.

[34]  Zhi-Hua Zhou,et al.  Enhancing relevance feedback in image retrieval using unlabeled data , 2006, ACM Trans. Inf. Syst..

[35]  John C. Platt,et al.  Semi-Supervised Learning with Conditional Harmonic Mixing , 2006, Semi-Supervised Learning.

[36]  Alexander Zien,et al.  An Augmented PAC Model for Semi-Supervised Learning , 2006 .

[37]  Maria-Florina Balcan,et al.  An Augmented PAC Model for Semi-Supervised Learning , 2006, Semi-Supervised Learning.

[38]  Nitesh V. Chawla,et al.  Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains , 2011, J. Artif. Intell. Res..

[39]  Yuan Qi,et al.  Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification , 2005, NIPS.

[40]  Tong Zhang,et al.  Analysis of Spectral Kernel Design based Semi-supervised Learning , 2005, NIPS.

[41]  John Shawe-Taylor,et al.  Two view learning: SVM-2K, Theory and Practice , 2005, NIPS.

[42]  Mikhail Belkin,et al.  Maximum Margin Semi-Supervised Learning for Structured Variables , 2005, NIPS 2005.

[43]  Tong Zhang,et al.  A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , 2005, J. Mach. Learn. Res..

[44]  Wei Chu,et al.  Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..

[45]  Zhi-Hua Zhou,et al.  Tri-training: exploiting unlabeled data using three classifiers , 2005, IEEE Transactions on Knowledge and Data Engineering.

[46]  Alon Orlitsky,et al.  Estimating and computing density based distance metrics , 2005, ICML.

[47]  Mikhail Belkin,et al.  Beyond the point cloud: from transductive to semi-supervised learning , 2005, ICML.

[48]  Xiaojin Zhu,et al.  Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning , 2005, ICML.

[49]  Bernhard Schölkopf,et al.  Learning from labeled and unlabeled data on a directed graph , 2005, ICML.

[50]  Zhi-Hua Zhou,et al.  Semi-Supervised Regression with Co-Training , 2005, IJCAI.

[51]  Dale Schuurmans,et al.  Unsupervised and Semi-Supervised Multi-Class Support Vector Machines , 2005, AAAI.

[52]  Hwee Tou Ng,et al.  Word Sense Disambiguation with Semi-Supervised Learning , 2005, AAAI.

[53]  Naonori Ueda,et al.  A Hybrid Generative/Discriminative Approach to Semi-Supervised Classifier Design , 2005, AAAI.

[54]  Wei Li,et al.  Semi-Supervised Sequence Modeling with Syntactic Topic Models , 2005, AAAI.

[55]  Maria-Florina Balcan,et al.  A PAC-Style Model for Learning from Labeled and Unlabeled Data , 2005, COLT.

[56]  Matti Kääriäinen,et al.  Generalization Error Bounds Using Unlabeled Data , 2005, COLT.

[57]  Dong-Hong Ji,et al.  Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning , 2005, ACL.

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[59]  Martial Hebert,et al.  Semi-Supervised Self-Training of Object Detection Models , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[60]  Nicolas Le Roux,et al.  Efficient Non-Parametric Function Induction in Semi-Supervised Learning , 2004, AISTATS.

[61]  Rosie Jones,et al.  Learning to Extract Entities from Labeled and Unlabeled Text , 2005 .

[62]  Ronald Rosenfeld,et al.  Semi-supervised learning with graphs , 2005 .

[63]  Mikhail Belkin,et al.  A Co-Regularization Approach to Semi-supervised Learning with Multiple Views , 2005 .

[64]  M. Griebel,et al.  Semi-supervised learning with sparse grids , 2005, ICML 2005.

[65]  Alex Holub,et al.  Exploiting Unlabelled Data for Hybrid Object Classification , 2005 .

[66]  Maria-Florina Balcan,et al.  Person Identification in Webcam Images: An Application of Semi-Supervised Learning , 2005 .

[67]  Kai Yu.  Blockwise Supervised Inference on Large Graphs , 2005 .

[68]  C. Oliveira.  Splitting the Unsupervised and Supervised Components of Semi-Supervised Learning , 2005 .

[69]  Kilian Q. Weinberger,et al.  Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization , 2005, AISTATS.

[70]  Mikhail Belkin,et al.  Linear Manifold Regularization for Large Scale Semi-supervised Learning , 2005 .

[71]  Alexander Zien,et al.  Semi-Supervised Classification by Low Density Separation , 2005, AISTATS.

[72]  Miguel Á. Carreira-Perpiñán,et al.  Proximity Graphs for Clustering and Manifold Learning , 2004, NIPS.

[73]  Thomas L. Griffiths,et al.  Integrating Topics and Syntax , 2004, NIPS.

[74]  Neil D. Lawrence,et al.  Semi-supervised Learning via Gaussian Processes , 2004, NIPS.

[75]  Maria-Florina Balcan,et al.  Co-Training and Expansion: Towards Bridging Theory and Practice , 2004, NIPS.

[76]  Ulrike von Luxburg,et al.  Limits of Spectral Clustering , 2004, NIPS.

[77]  Thomas Hofmann,et al.  Semi-supervised Learning on Directed Graphs , 2004, NIPS.

[78]  Ji Zhu,et al.  A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning , 2004, NIPS.

[79]  Zoubin Ghahramani,et al.  Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning , 2004, NIPS.

[80]  Yoshua Bengio,et al.  Semi-supervised Learning by Entropy Minimization , 2004, CAP.

[81]  Adrian Corduneanu,et al.  Distributed Information Regularization on Graphs , 2004, NIPS.

[82]  Zhi-Hua Zhou,et al.  Exploiting Unlabeled Data in Content-Based Image Retrieval , 2004, ECML.

[83]  Dani Lischinski,et al.  Colorization using optimization , 2004, ACM Trans. Graph..

[84]  Rebecca Hwa,et al.  Co-training for Predicting Emotions with Spoken Dialogue Data , 2004, ACL.

[85]  Chris Callison-Burch,et al.  Statistical Machine Translation with Word- and Sentence-Aligned Parallel Corpora , 2004, ACL.

[86]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[87]  Xiaojin Zhu,et al.  Kernel conditional random fields: representation and clique selection , 2004, ICML.

[88]  John D. Lafferty,et al.  Semi-supervised learning using randomized mincuts , 2004, ICML.

[89]  Kilian Q. Weinberger,et al.  Learning a kernel matrix for nonlinear dimensionality reduction , 2004, ICML.

[90]  Mikhail Belkin,et al.  Regularization and Semi-supervised Learning on Large Graphs , 2004, COLT.

[91]  Kilian Q. Weinberger,et al.  Unsupervised Learning of Image Manifolds by Semidefinite Programming , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[92]  Gareth Funka-Lea,et al.  Multi-label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials , 2004, ECCV Workshops CVAMIA and MMBIA.

[93]  Dale Schuurmans,et al.  Metric-Based Methods for Adaptive Model Selection and Regularization , 2002, Machine Learning.

[94]  Nello Cristianini,et al.  Latent Semantic Kernels , 2001, Journal of Intelligent Information Systems.

[95]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[96]  Pat Langley,et al.  Editorial: On Machine Learning , 1986, Machine Learning.

[97]  Amos Storkey,et al.  Advances in Neural Information Processing Systems 20 , 2007 .

[98]  Jitendra Malik,et al.  Spectral grouping using the Nystrom method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[99]  Nizar Grira,et al.  Unsupervised and Semi-supervised Clustering : a Brief Survey ∗ , 2004 .

[100]  Tom Michael Mitchell,et al.  The Role of Unlabeled Data in Supervised Learning , 2004 .

[101]  Mikhail Belkin,et al.  Manifold Regularization : A Geometric Framework for Learning from Examples , 2004 .

[102]  Kai Yu.  Semi-supervised Induction with Basis Functions , 2004 .

[103]  Nello Cristianini,et al.  Convex Methods for Transduction , 2003, NIPS.

[104]  Bernhard Schölkopf,et al.  Ranking on Data Manifolds , 2003, NIPS.

[105]  Bernhard Schölkopf,et al.  Learning with Local and Global Consistency , 2003, NIPS.

[106]  Thomas L. Griffiths,et al.  Semi-Supervised Learning with Trees , 2003, NIPS.

[107]  Matthias Hein,et al.  Measure Based Regularization , 2003, NIPS.

[108]  Ben Taskar,et al.  Max-Margin Markov Networks , 2003, NIPS.

[109]  Lawrence K. Saul,et al.  Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold , 2003, J. Mach. Learn. Res..

[110]  J. Lafferty,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[111]  Zoubin Ghahramani,et al.  Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.

[112]  Thorsten Joachims,et al.  Transductive Learning via Spectral Graph Partitioning , 2003, ICML.

[113]  Fabio Gagliardi Cozman,et al.  Semi-Supervised Learning of Mixture Models , 2003, ICML.

[114]  Bing Liu,et al.  Learning with Positive and Unlabeled Examples Using Weighted Logistic Regression , 2003, ICML.

[115]  John Hart,et al.  ACM Transactions on Graphics: Editorial , 2003, SIGGRAPH 2003.

[116]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[117]  Ellen Riloff,et al.  Learning subjective nouns using extraction pattern bootstrapping , 2003, CoNLL.

[118]  D. Donoho,et al.  Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[119]  Adrian Corduneanu,et al.  On Information Regularization , 2002, UAI.

[120]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[121]  Alexander J. Smola,et al.  Kernels and Regularization on Graphs , 2003, COLT.

[122]  Zoubin Ghahramani,et al.  Semi-supervised learning : from Gaussian fields to Gaussian processes , 2003 .

[123]  Lise Getoor,et al.  Link-based Classifi-cation using Labeled and Unlabeled Data , 2003 .

[124]  Zoubin Ghahramani,et al.  Towards semi-supervised classification with Markov random fields , 2002 .

[125]  Stefan C. Kremer,et al.  Clustering unlabeled data with SOMs improves classification of labeled real-world data , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[126]  Philip S. Yu,et al.  Partially Supervised Classification of Text Documents , 2002, ICML.

[127]  Craig A. Knoblock,et al.  Active + Semi-supervised Learning = Robust Multi-View Learning , 2002, ICML.

[128]  John D. Lafferty,et al.  Diffusion Kernels on Graphs and Other Discrete Input Spaces , 2002, ICML.

[129]  Tommi S. Jaakkola,et al.  Information Regularization with Partially Labeled Data , 2002, NIPS.

[130]  Rémi Gilleron,et al.  Text Classification from Positive and Unlabeled Examples , 2002 .

[131]  Bernhard Schölkopf,et al.  Cluster Kernels for Semi-Supervised Learning , 2002, NIPS.

[132]  Yee Whye Teh,et al.  Automatic Alignment of Local Representations , 2002, NIPS.

[133]  Adrian Corduneanu,et al.  Stable Mixing of Complete and Incomplete Information , 2014 .

[134]  Avrim Blum,et al.  Learning from Labeled and Unlabeled Data using Graph Mincuts , 2001, ICML.

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[137]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[138]  Sanjoy Dasgupta,et al.  PAC Generalization Bounds for Co-training , 2001, NIPS.

[139]  Tommi S. Jaakkola,et al.  Partially labeled classification with Markov random walks , 2001, NIPS.

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[146]  Yan Zhou,et al.  Enhancing Supervised Learning with Unlabeled Data , 2000, ICML.

[147]  Tong Zhang,et al.  The Value of Unlabeled Data for Classification Problems , 2000, ICML 2000.

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