It is difficult to find qualified candidates for available positions, especially when there are a lot of candidates. Finding the right person at the right time can positively affect the success of the team. The labori...
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An approach to the composition of learning algorithms for classes of constant VC-dimension into learning algorithms for more complicated classes is presented. The composition theorem is proven for a broader set of cla...
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An approach to the composition of learning algorithms for classes of constant VC-dimension into learning algorithms for more complicated classes is presented. The composition theorem is proven for a broader set of classes C and for other learning models. It is shown that if a class of concepts C is exactly learnable in time t by a hypothesis class H of constant VC-dimension then the class C* is learnable in time polynomial in t and m. A much weaker condition is also shown that can be placed on C and H to ensure learnability of C* is shown. The composition theorem cannot be extended to classes with nonconstant VC-dimension.
This work is concerned with the establishment of a relation between the fields of qualitative reasoning (QR) and neural networks. We explore how well-known backpropagation learning algorithm can be studied from the po...
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Designing Net-Zero Energy Factories is a significant step towards sustainable industrial operations. Within industrial systems, the identification and exploitation of flexibility is a challenging task. This paper pres...
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Independent component analysis (ICA) has been applied in many fields of signal processing and many ICA learning algorithms have been proposed from different perspectives. However, there is still a lack of a deep mathe...
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We give a range of techniques to effectively apply on-line learning algorithms, such as Perceptron and Winnow, to both on-line and batch fusion problems. Our first technique is a new way to combine the predictions of ...
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ISBN:
(纸本)9783000248832
We give a range of techniques to effectively apply on-line learning algorithms, such as Perceptron and Winnow, to both on-line and batch fusion problems. Our first technique is a new way to combine the predictions of multiple hypotheses. These hypotheses are selected from the many hypotheses that are generated in the course of on-line learning. Our second technique is to save old instances and use them for extra updates on the current hypothesis. These extra updates can decrease the number of mistakes made on new instances. Both techniques keep the algorithms efficient and allow the algorithms to learn in the presence of large amounts of noise.
In this paper, we wanted to compare distance metric-learning algorithms on UCI datasets. We wanted to assess the accuracy of these algorithms in many situations, perhaps some that they were not initially designed for....
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ISBN:
(纸本)9781605586731
In this paper, we wanted to compare distance metric-learning algorithms on UCI datasets. We wanted to assess the accuracy of these algorithms in many situations, perhaps some that they were not initially designed for. We looked for many algorithms and chose four of them based on our criteria. We also selected six UCI datasets. From the data's labels, we create similarity dataset that will be used to train and test the algorithms. The nature of each dataset is different (size, dimension), and the algorithms' results may vary because of these parameters. We also wanted to have some robust algorithms on dataset whose similarity is not perfect, whose the labels are no well defined. This occurs in multi-labeled datasets or even worse in human-built ones. To simulate this, we injected contradictory data and observed the behavior of the algorithms. This study seeks for a reliable algorithm in such scenarios keeping in mind future uses in recommendation processes. Copyright 2009 ACM.
We study the worst-case behavior of a family of learning algorithms based on Sutton's [7] method of temporal differences. In our on-line learning framework, learning takes place in a sequence of trials, and the go...
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Construction projects generally have the characteristics of large scale, many construction processes and complicated handover, and all these links may have a certain impact on the quality of engineering construction. ...
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ISOMAP, LLE, Laplacian Eigenmaps and LTSA are several representative manifold learning algorithms. In most of manifold learning methods, there are two free parameters: the neighborhood size and the intrinsic dimension...
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