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1,351,649
Instance-based learning algorithms
- Machine Learning
, 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
Abstract
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Cited by 1359 (18 self)
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to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances
Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories
, 2004
"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."
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Cited by 770 (17 self)
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Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been
Locally weighted learning
- ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Multitask Learning
- MACHINE LEARNING
, 1997
"... Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task ..."
Abstract
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Cited by 661 (6 self)
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Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task
A learning algorithm for Boltzmann machines
- Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
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Cited by 586 (13 self)
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to a gen-eral learning rule for modifying the connection strengths so as to incorporate knowledge obout o task domain in on efficient way. We describe some simple examples in which the learning algorithm creates internal representations thot ore demonstrobly the most efficient way of using
The eyes have it: A task by data type taxonomy for information visualizations
- IN IEEE SYMPOSIUM ON VISUAL LANGUAGES
, 1996
"... A useful starting point for designing advanced graphical user interjaces is the Visual lnformation-Seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been ..."
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Cited by 1250 (28 self)
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proposed in recent years. This paper offers a task by data type taxonomy with seven data types (one-, two-, three-dimensional datu, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, Zoom, filter, details-on-demand, relate, history, and extracts).
Machine Learning in Automated Text Categorization
- ACM COMPUTING SURVEYS
, 2002
"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."
Abstract
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Cited by 1658 (22 self)
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to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual
Parallel Networks that Learn to Pronounce English Text
- COMPLEX SYSTEMS
, 1987
"... This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed h ..."
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Cited by 548 (5 self)
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This paper describes NETtalk, a class of massively-parallel network systems that learn to convert English text to speech. The memory representations for pronunciations are learned by practice and are shared among many processing units. The performance of NETtalk has some similarities with observed
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
, 1989
"... The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a precis ..."
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Cited by 529 (4 self)
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The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a
Designing Learning
- In
, 2004
"... …Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer ..."
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Cited by 555 (9 self)
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…Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer
Results 1 - 10
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