Results 231 to 240 of about 219,224 (269)
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Knowledge and Information Systems, 2006
This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM).
Dacheng Tao +4 more
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This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM).
Dacheng Tao +4 more
openaire +1 more source
2008
Supervised learning accounts for a lot of research activity in machine learning and many supervised learning techniques have found application in the processing of multimedia content. The defining characteristic of supervised learning is the availability of annotated training data. The name invokes the idea of a 'supervisor' that instructs the learning
Cunningham, Pádraig +2 more
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Supervised learning accounts for a lot of research activity in machine learning and many supervised learning techniques have found application in the processing of multimedia content. The defining characteristic of supervised learning is the availability of annotated training data. The name invokes the idea of a 'supervisor' that instructs the learning
Cunningham, Pádraig +2 more
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ASCENT: Active Supervision for Semi-Supervised Learning
IEEE Transactions on Knowledge and Data Engineering, 2020Active learning algorithms attempt to overcome the labeling bottleneck by asking queries from large collection of unlabeled examples. Existing batch mode active learning algorithms suffer from three limitations: (1) The methods that are based on similarity function or optimizing certain diversity measurement, in which may lead to suboptimal performance
Yanchao Li 0001 +5 more
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2013
Traditional active learning selects the most informative (e.g., the most uncertain) example and queries an oracle for the label. However, as more examples are learned in the process, even the most uncertain examples can become certain. In this case, would it be better to make predictions directly and take the consequence if the prediction is wrong ...
Eileen A. Ni, Da Kuang, Charles X. Ling
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Traditional active learning selects the most informative (e.g., the most uncertain) example and queries an oracle for the label. However, as more examples are learned in the process, even the most uncertain examples can become certain. In this case, would it be better to make predictions directly and take the consequence if the prediction is wrong ...
Eileen A. Ni, Da Kuang, Charles X. Ling
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Supervised Learning for Classification
2005Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method.
Hongyu Li 0001 +2 more
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Ubiquitously Supervised Subspace Learning
IEEE Transactions on Image Processing, 2009In this paper, our contributions to the subspace learning problem are two-fold. We first justify that most popular subspace learning algorithms, unsupervised or supervised, can be unitedly explained as instances of a ubiquitously supervised prototype. They all essentially minimize the intraclass compactness and at the same time maximize the interclass ...
Jianchao Yang +2 more
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Neural Computation, 1993
Factorial learning, finding a statistically independent representation of a sensory “image”—a factorial code—is applied here to solve multilayer supervised learning problems that have traditionally required backpropagation. This lends support to Barlow's argument for factorial sensory processing, by demonstrating how it can solve actual pattern ...
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Factorial learning, finding a statistically independent representation of a sensory “image”—a factorial code—is applied here to solve multilayer supervised learning problems that have traditionally required backpropagation. This lends support to Barlow's argument for factorial sensory processing, by demonstrating how it can solve actual pattern ...
openaire +1 more source
Supervised and Semi-supervised Machine Learning Ranking
2007We present a Semi-supervised Machine Learning based ranking model which can automatically learn its parameters using a training set of a few labeled and unlabeled examples composed of queries and relevance judgments on a subset of the document elements.
Vittaut, Jean-Noël, Gallinari, Patrick
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