The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Journal of Machine Learning Research
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Convex Optimization
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Journal of Artificial Intelligence Research
Stability and generalization of bipartite ranking algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
A boosting algorithm for learning bipartite ranking functions with partially labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank with partially-labeled data
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Image Tagging Using PageRank over Bipartite Graphs
Proceedings of the 30th DAGM symposium on Pattern Recognition
Web page classification: Features and algorithms
ACM Computing Surveys (CSUR)
Incremental learning to rank with partially-labeled data
Proceedings of the 2009 workshop on Web Search Click Data
An efficient algorithm for learning to rank from preference graphs
Machine Learning
Towards a semantic self-organising web page-ranking mechanism using computational geometry
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Personalized tag recommendation using graph-based ranking on multi-type interrelated objects
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Simulated Iterative Classification A New Learning Procedure for Graph Labeling
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Learning Preferences with Hidden Common Cause Relations
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Semi-supervised ensemble ranking
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
ROSE: retail outlet site evaluation by learning with both sample and feature preference
Proceedings of the 18th ACM conference on Information and knowledge management
Semi-supervised graph-ranking for text retrieval
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
International Journal of Knowledge and Web Intelligence
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Semi-supervised ranking for document retrieval
Computer Speech and Language
Conditional ranking on relational data
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Ranking on large-scale graphs with rich metadata
Proceedings of the 20th international conference companion on World wide web
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Semi-supervised ranking on very large graphs with rich metadata
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Using rich social media information for music recommendation via hypergraph model
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
The machine learning and traveling repairman problem
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Semi-supervised learning to rank with preference regularization
Proceedings of the 20th ACM international conference on Information and knowledge management
Classification and annotation in social corpora using multiple relations
Proceedings of the 20th ACM international conference on Information and knowledge management
A probabilistic diffusion scheme for anomaly detection on smartphones
WISTP'10 Proceedings of the 4th IFIP WG 11.2 international conference on Information Security Theory and Practices: security and Privacy of Pervasive Systems and Smart Devices
A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval
ACM Transactions on Information Systems (TOIS)
Magnet community identification on social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale graph mining and learning for information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Information Sciences: an International Journal
Image ranking via attribute boosted hypergraph
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
| Hi-index | 0.00 |
In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a real-valued ranking function that induces a ranking or ordering over the object space. We consider the problem of learning such a ranking function when the data is represented as a graph, in which vertices correspond to objects and edges encode similarities between objects. Building on recent developments in regularization theory for graphs and corresponding Laplacian-based methods for classification, we develop an algorithmic framework for learning ranking functions on graph data. We provide generalization guarantees for our algorithms via recent results based on the notion of algorithmic stability, and give experimental evidence of the potential benefits of our framework.