@inproceedings{cone2006,
        title = {Cone Cluster Labeling for Support Vector Clustering},
        author = {Sei-Hyung Lee and Karen M. Daniels},
        booktitle = {Proceedings of 6th SIAM Conference on Data Mining},
        month = {May},
        pages = {484–488},
        year = 2006,
        url = {http://www.siam.org/meetings/sdm06/proceedings/046lees.pdf},
        added = {2007-04-29 16:58:13 +0200},
	modified = {2007-06-19 18:52:22 +0200},
	description = {BibSonomy::edit bibtex},
	biburl = {http://www.bibsonomy.org/bibtex/276d1018ba398695e454d20de302de6e6/hotho},
	keywords = {toread code SVM clustering}
}

@article{keyhere,
        title = {A Cost-Sensitive Paradigm for Multiclass to Binary Decomposition Schemes},
        author = {Claudio Marrocco and Francesco Tortorella},
        journal = {Structural, Syntactic, and Statistical Pattern Recognition},
        pages = {753--761},
        year = 2004,
        url = {http://www.springerlink.com/content/5fdg88yxqvwale7j},
        description = {SpringerLink - Book Chapter},
	abstract = {An established technique to face a multiclass categorization problem is to reduce it into a set of two-class problems. To this aim, the main decomposition schemes employed are one vs. one, one vs. all and Error Correcting Output Coding. A point not yet considered in the research is how to apply these methods to a cost-sensitive classification that represents a significant aspect in many real problems. In this paper we propose a novel method which, starting from the cost matrix for the multi-class problem and from the code matrix employed, extracts a cost matrix for each of the binary subproblems induced by the coding matrix. In this way, it is possible to tune the single two-class classifier according to the cost matrix obtained and achieve an output from all the dichotomizers which takes into account the requirements of the original multi-class cost matrix. To evaluate the effectiveness of the method, a large number of tests has been performed on real data sets. The experiments results have shown a significant improvement in terms of classification cost, specially when using the ECOC scheme.
ER  -},
	biburl = {http://www.bibsonomy.org/bibtex/2a234beda6a9a042041c89b21c8291eb0/hotho},
	keywords = {class multi svm classifier}
}

@article{keyhere,
        title = {kNN Versus SVM in the Collaborative Filtering Framework},
        author = {Miha Grčar and Blaž Fortuna and Dunja Mladenič and Marko Grobelnik},
        journal = {Data Science and Classification},
        pages = {251--260},
        year = 2006,
        url = {http://db.cs.ualberta.ca/webkdd05/proc/paper25-mladenic.pdf},
        doi = {http://dx.doi.org/10.1007/3-540-34416-0_27},
	description = {SpringerLink - Book Chapter},
	abstract = {We present experimental results of confronting the k-Nearest Neighbor (kNN) algorithm with Support Vector Machine (SVM) in
the collaborative filtering framework using datasets with different properties. While k-Nearest Neighbor is usually used forthe collaborative filtering tasks, Support Vector Machine is considered a state-of-the-art classification algorithm. Sincecollaborative filtering can also be interpreted as a classification/regression task, virtually any supervised learning algorithm(such as SVM) can also be applied. Experiments were performed on two standard, publicly available datasets and, on the otherhand, on a real-life corporate dataset that does not fit the profile of ideal data for collaborative filtering. We concludethat the quality of collaborative filtering recommendations is highly dependent on the quality of the data. Furthermore, wecan see that kNN is dominant over SVM on the two standard datasets. On the real-life corporate dataset with high level ofsparsity, kNN fails as it is unable to form reliable neighborhoods. In this case SVM outperforms kNN.},
	biburl = {http://www.bibsonomy.org/bibtex/249c80c0aeb3c7eeb1941dcb62a5d26f3/hotho},
	keywords = {recommender knn learning svm}
}

@inproceedings{bunescu-etal-2006,
        title = {Using Encyclopedic Knowledge for Named Entity Disambiguation },
        author = {Razvan Bunescu and Marius Pasca},
        booktitle = {Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL-06), Trento, Italy},
        month = {April},
        pages = {9-16},
        year = 2006,
        url = {http://www.cs.utexas.edu/~ml/publication/paper.cgi?paper=encyc-eacl-06.ps.gz},
        biburl = {http://www.bibsonomy.org/bibtex/2cc98e8aa7e3fb7d8addc0ec4fe45f7d2/hotho},
	keywords = {disambiguation named svm wikipedia entity folksonomy kernel}
}

@article{schoelkopf01kernelbased,
        title = {An Introduction to Kernel-Based learning Algorithms},
        author = {K.-R. Müller and S. Mika and G. Rätsch and S. Tsuda and B Schölkopf},
        journal = {IEEE Transactions on Neural Networks},
        number = 2,
        pages = {181--202},
        volume = 12,
        year = 2001,
        url = {http://www.ist.temple.edu/~vucetic/cis526fall2003/SVMintro.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/28083bda55e1a6aab3d0b091ce56d699a/hotho},
	keywords = {classification svm learning kernel}
}