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5,520
Learning with Multiple Labels
, 2003
"... In this paper, we study a special kind of learning problem in which each training instance is given a set of (or distribution over) candidate class labels and only one of the candidate labels is the correct one. Such a problem can occur, e.g., in an information retrieval setting where a set of w ..."
Abstract
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Cited by 63 (0 self)
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In this paper, we study a special kind of learning problem in which each training instance is given a set of (or distribution over) candidate class labels and only one of the candidate labels is the correct one. Such a problem can occur, e.g., in an information retrieval setting where a set
On Multiple Kernel Learning with Multiple Labels
"... For classification with multiple labels, a common approach is to learn a classifier for each label. With a kernel-based classifier, there are two options to set up kernels: select a specific kernel for each label or the same kernel for all labels. In this work, we present a unified framework for mul ..."
Abstract
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Cited by 20 (2 self)
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For classification with multiple labels, a common approach is to learn a classifier for each label. With a kernel-based classifier, there are two options to set up kernels: select a specific kernel for each label or the same kernel for all labels. In this work, we present a unified framework
LabelMe: A Database and Web-Based Tool for Image Annotation
, 2008
"... We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant sha ..."
Abstract
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Cited by 679 (46 self)
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We seek to build a large collection of images with ground truth labels to be used for object detection and recognition research. Such data is useful for supervised learning and quantitative evaluation. To achieve this, we developed a web-based tool that allows easy image annotation and instant
On the Multiple Label Placement Problem
- IN PROC. 10TH CANADIAN CONF. COMPUTATIONAL GEOMETRY (CCCG'98
, 1998
"... We consider the problem of positioning text or symbol labels associated with graphical features of two dimensional maps (geographical or technical) or drawings. In many practical applications, it is often the case, that each graphical feature has more than one label. This variation of the labeling p ..."
Abstract
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Cited by 9 (1 self)
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We consider the problem of positioning text or symbol labels associated with graphical features of two dimensional maps (geographical or technical) or drawings. In many practical applications, it is often the case, that each graphical feature has more than one label. This variation of the labeling
Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation
, 2002
"... There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is ..."
Abstract
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Cited by 718 (9 self)
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There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment
Max-margin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
Abstract
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Cited by 604 (15 self)
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the ability to use high-dimensional feature spaces, and from their strong theoretical guarantees. However, many real-world tasks involve sequential, spatial, or structured data, where multiple labels must be assigned. Existing kernel-based methods ignore structure in the problem, assigning labels
AFNI: software for analysis and visualization of functional magnetic resonance neuroimages
- Computers and Biomedical Research
, 1996
"... email rwcoxmcwedu A package of computer programs for analysis and visualization of threedimensional human brain functional magnetic resonance imaging FMRI results is described The software can color overlay neural activation maps onto higher resolution anatomical scans Slices in each cardinal pl ..."
Abstract
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Cited by 807 (3 self)
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plane can be viewed simultaneously Manual placement of markers on anatom ical landmarks allows transformation of anatomical and functional scans into stereotaxic TalairachTournoux coordinates The techniques for automatically generating transformed functional data sets from manually labeled anatomical
What is a hidden Markov model?
, 2004
"... Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with ho-mologous resi ..."
Abstract
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Cited by 1344 (8 self)
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Often, problems in biological sequence analysis are just a matter of putting the right label on each residue. In gene identification, we want to label nucleotides as exons, introns, or intergenic sequence. In sequence alignment, we want to associate residues in a query sequence with ho
A framework for learning predictive structures from multiple tasks and unlabeled data
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods ar ..."
Abstract
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Cited by 443 (3 self)
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One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semi-supervised learning. Although a number of such methods
Multiple Labeling and Time-Resolvable Fluorophores
, 1991
"... conventional fluerophorea Small (28 nm for fluorescein) ..."
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