Results 11 - 20
of
1,278
On kernel-target alignment
- Advances in Neural Information Processing Systems 14
, 2002
"... Editor: Kernel based methods are increasingly being used for data modeling because of their conceptual simplicity and outstanding performance on many tasks. However, the kernel function is often chosen using trial-and-error heuristics. In this paper we address the problem of measuring the degree of ..."
Abstract
-
Cited by 300 (8 self)
- Add to MetaCart
Editor: Kernel based methods are increasingly being used for data modeling because of their conceptual simplicity and outstanding performance on many tasks. However, the kernel function is often chosen using trial-and-error heuristics. In this paper we address the problem of measuring the degree of agreement between a kernel and a learning task. A quantitative measure of agreement is important from both a theoretical and practical point of view. We propose a quantity to capture this notion, which we call Alignment. We study its theoretical properties, and derive a series of simple algorithms for adapting a kernel to the labels and vice versa. This produces a series of novel methods for clustering and transduction, kernel combination and kernel selection. The algorithms are tested on two publicly available datasets and are shown to exhibit good performance.
Sparseness of support vector machines
, 2003
"... Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our w ..."
Abstract
-
Cited by 263 (35 self)
- Add to MetaCart
Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our way we prove several results which are of great importance for the understanding of SVMs. In particular, we describe to which “limit” SVM decision functions tend, discuss the corresponding notion of convergence and provide some results on the stability of SVMs using subdifferential calculus in the associated reproducing kernel Hilbert space.
Extremely Randomized Trees
- MACHINE LEARNING
, 2003
"... This paper presents a new learning algorithm based on decision tree ensembles. In opposition to the classical decision tree induction method, the trees of the ensemble are built by selecting the tests during their induction fully at random. This extreme ..."
Abstract
-
Cited by 262 (49 self)
- Add to MetaCart
This paper presents a new learning algorithm based on decision tree ensembles. In opposition to the classical decision tree induction method, the trees of the ensemble are built by selecting the tests during their induction fully at random. This extreme
Multicategory Support Vector Machines, theory, and application to the classification of microarray data and satellite radiance data
- Journal of the American Statistical Association
, 2004
"... Two-category support vector machines (SVM) have been very popular in the machine learning community for classi � cation problems. Solving multicategory problems by a series of binary classi � ers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We pro ..."
Abstract
-
Cited by 261 (25 self)
- Add to MetaCart
Two-category support vector machines (SVM) have been very popular in the machine learning community for classi � cation problems. Solving multicategory problems by a series of binary classi � ers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We propose the multicategory support vector machine (MSVM), which extends the binary SVM to the multicategory case and has good theoretical properties. The proposed method provides a unifying framework when there are either equal or unequal misclassi � cation costs. As a tuning criterion for the MSVM, an approximate leave-one-out cross-validation function, called Generalized Approximate Cross Validation, is derived, analogous to the binary case. The effectiveness of the MSVM is demonstrated through the applications to cancer classi � cation using microarray data and cloud classi � cation with satellite radiance pro � les.
Dependency tree kernels for relation extraction
- In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04
, 2004
"... We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility ..."
Abstract
-
Cited by 254 (2 self)
- Add to MetaCart
We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20 % F1 improvement over a “bag-of-words ” kernel. 1
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
- Applications of Data Mining in Computer Security
, 2002
"... Abstract Most current intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is typically expensive to produce. We present a new geometric framework for unsupervised anomaly detection, which are algorithms that ..."
Abstract
-
Cited by 232 (9 self)
- Add to MetaCart
(Show Context)
Abstract Most current intrusion detection systems employ signature-based methods or data mining-based methods which rely on labeled training data. This training data is typically expensive to produce. We present a new geometric framework for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data. In our framework, data elements are mapped to a feature space which is typically a vector space! d. Anomalies are detected by determining which points lies in sparse
A statistical approach to texture classification from single images
- International Journal of Computer Vision
, 2005
"... ..."
A Kernel Method for Multi-Labelled Classification
- In Advances in Neural Information Processing Systems 14
, 2001
"... This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margi ..."
Abstract
-
Cited by 228 (0 self)
- Add to MetaCart
(Show Context)
This article presents a Support Vector Machine (SVM) like learning system to handle multi-label problems. Such problems are usually decomposed into many two-class problems but the expressive power of such a system can be weak [5, 7]. We explore a new direct approach. It is based on a large margin ranking system that shares a lot of common properties with SVMs. We tested it on a Yeast gene functional classification problem with positive results.
Kernel Methods for Relation Extraction
, 2002
"... We present an application of kernel methods to extracting relations from unstructured natural language sources. ..."
Abstract
-
Cited by 215 (0 self)
- Add to MetaCart
We present an application of kernel methods to extracting relations from unstructured natural language sources.
Combining pairwise sequence similarity and support vector machines for remote protein homology detection
- Proc. 6th Ann. Int. Conf. Computational Molecular Biology
, 2002
"... One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated protein is via sequence similarity with one or more prote ..."
Abstract
-
Cited by 211 (21 self)
- Add to MetaCart
(Show Context)
One key element in understanding the molecular machinery of the cell is to understand the structure and function of each protein encoded in the genome. A very successful means of inferring the structure or function of a previously unannotated protein is via sequence similarity with one or more proteins whose structure or function is already known. Toward this end, we propose a means of representing proteins using pairwise sequence similarity scores. This representation, combined with a discriminative classi � cation algorithm known as the support vector machine (SVM), provides a powerful means of detecting subtle structural and evolutionary relationships among proteins. The algorithm, called SVM-pairwise, when tested on its ability to recognize previously unseen families from the SCOP database, yields signi � cantly better performance than SVM-Fisher, pro � le HMMs, and PSI-BLAST. Key words: pairwise sequence comparison, homology, detection, support vector machines. 1.
