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The Nature of Statistical Learning Theory

by Vladimir N. Vapnik , 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
Abstract - Cited by 12976 (32 self) - Add to MetaCart
Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

Solving multiclass learning problems via error-correcting output codes

by Thomas G. Dietterich, Ghulum Bakiri - JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH , 1995
"... Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass l ..."
Abstract - Cited by 730 (8 self) - Add to MetaCart
Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k>2values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hx i;f(x i)i. Existing approaches to multiclass

Cognitive load during problem solving: effects on learning

by John Sweller - COGNITIVE SCIENCE , 1988
"... Considerable evidence indicates that domain specific knowledge in the form of schemes is the primary factor distinguishing experts from novices in problem-solving skill. Evidence that conventional problem-solving activity is not effective in schema acquisition is also accumulating. It is suggested t ..."
Abstract - Cited by 603 (13 self) - Add to MetaCart
that a major reason for the ineffectiveness of problem solving as a learning device, is that the cognitive processes required by the two activities overlap insufficiently, and that conventional problem solving in the form of means-ends analysis requires a relatively large amount of cognitive processing

Multitask Learning

by Rich Caruana - 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 - Cited by 661 (6 self) - Add to MetaCart
an algorithm for multitask learning in decision trees. Because multitask learning works, can be applied to many different kinds of domains, and can be used with different learning algorithms, we conjecture there will be many opportunities for its use on real-world problems.

Ant Colony System: A cooperative learning approach to the traveling salesman problem

by Marco Dorigo, Luca Maria Gambardella - IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION , 1997
"... This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSP’s. Ants cooperate using an indirect form of communication mediated by a pher ..."
Abstract - Cited by 1000 (53 self) - Add to MetaCart
This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSP’s. Ants cooperate using an indirect form of communication mediated by a

Designing Learning

by Ann C. Baker, Patricia J. Jensen, David A. Kolb, D. A. Conversational, Ann Baker, Patricia Jensen, David Kolb - 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 ..."
Abstract - Cited by 555 (9 self) - Add to MetaCart
…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

Learning low-level vision

by William T. Freeman, Egon C. Pasztor - International Journal of Computer Vision , 2000
"... We show a learning-based method for low-level vision problems. We set-up a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently prop ..."
Abstract - Cited by 586 (31 self) - Add to MetaCart
We show a learning-based method for low-level vision problems. We set-up a Markov network of patches of the image and the underlying scene. A factorization approximation allows us to easily learn the parameters of the Markov network from synthetic examples of image/scene pairs, and to e ciently

What Video Games Have to Teach us About learning and Literacy

by James Paul Gee
"... Xenosaga: Episode 1 are learning machines. They get themselves learned and learned well, so that they get played long and hard by a great many people. This is how they and their designers survive and perpetuate themselves. If a game cannot be learned and even mastered at a certain level, it won’t ge ..."
Abstract - Cited by 1074 (16 self) - Add to MetaCart
’t get played by enough people, and the company that makes it will go broke. Good learning in games is a capitalist-driven Darwinian process of selection of the fittest. Of course, game designers could have solved their learning problems by making games shorter and easier, by dumbing them down, so

Distance metric learning for large margin nearest neighbor classification

by Kilian Q. Weinberger, John Blitzer, Lawrence K. Saul - In NIPS , 2006
"... We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
Abstract - Cited by 685 (15 self) - Add to MetaCart
. On seven data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification—for example, achieving a test error rate of 1.3 % on the MNIST handwritten digits. As in support vector machines (SVMs), the learning problem reduces to a

Learning with local and global consistency

by Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf - Advances in Neural Information Processing Systems 16 , 2004
"... We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic stru ..."
Abstract - Cited by 666 (21 self) - Add to MetaCart
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic
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