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4,168
Combining A Priori Knowledge And
"... The problem of updating the global position of an autonomous vehicle is considered. An iterative procedure is proposed to fit a map to a set of noisy point measurements. The procedure is inspired by a nonparametric procedure for probability density function mode searching. ..."
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The problem of updating the global position of an autonomous vehicle is considered. An iterative procedure is proposed to fit a map to a set of noisy point measurements. The procedure is inspired by a nonparametric procedure for probability density function mode searching.
Template Attacks Based On Priori Knowledge
"... Abstract. Template Attacks consist of two stages, the profiling stage and the extraction stage. In order to improve the classification perfor-mance of Template Attacks, a feasible and usual way is to characterize signals and noises more accurately. Under the assumption that a refer-ence device is fu ..."
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, this is not always the case and the attacker can only have access to a limited number of actual power traces. In this paper, we show that the attacker can still make Template Attacks practical and more powerful in the above scenario if he could obtain the priori knowl-edge about the reference device. The priori
De Re A Priori Knowledge
"... Please cite published version Abstract: Suppose a sentence of the following form is true in a certain context: ‘Necessarily, whenever one believes that the F is uniquely F if anything is, and x is the F, one believes that x is uniquely F if anything is’. I argue that almost always, in such a case, t ..."
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, the sentences that result when both occurrences of ‘believes ’ are replaced with ‘has justification to believe’, ‘knows’, or ‘knows a priori ’ will also be true in the same context. I also argue that many sentences of the relevant form are true in ordinary contexts, and conclude that a priori knowledge
Design of Neural Networks Using a Priori Knowledge
, 1995
"... We investigate methods for the design of neural networks using application specific knowledge. The standard approach of problem solving with neural networks reduces a problem to an approximation or classification task, which is solved by learning from observation data. However, application specific ..."
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Cited by 4 (0 self)
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knowledge that is not available as observation data is usually ignored. This inefficient usage of information cannot be afforded for complex problems. An integration of both observation data and a#priori knowledge into neural networks should be tried. We show that using a#priori knowledge for the design
A priori Knowledge and the Kochen-Specker Theorem
, 2008
"... We introduce and formalize a notion of “a priori knowledge ” about a quantum system, and show some properties about this form of knowledge. Finally, we show that the Kochen-Specker theorem follows directly from this study. 1 ..."
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Cited by 9 (7 self)
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We introduce and formalize a notion of “a priori knowledge ” about a quantum system, and show some properties about this form of knowledge. Finally, we show that the Kochen-Specker theorem follows directly from this study. 1
Classifier Combination: The Role Of A-Priori Knowledge
- IWFHR VII
, 2000
"... level combination methods use the top candidate provided by each classifier [4,5,6] ; Ranked-level combination methods use the entire ranked list of candidates [7,8] ; Measurement-level combination methods use also the confidence value of each candidate in the ranked list [9,10]. Among the others ..."
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Cited by 4 (0 self)
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the others, classifier combination at abstract-level is the most general approach since every classifier is able at least to provide results at abstract level. In the process of classifier combination, some kind of a-priori knowledge can also be used in order to achieve better performance. On the basis
Semidefinite Clustering for Image Segmentation with A-priori Knowledge
"... Abstract. Graph-based clustering methods are successfully applied to computer vision and machine learning problems. In this paper we demonstrate how to introduce a-priori knowledge on class membership in a systematic and principled way: starting from a convex relaxation of the graph-based clustering ..."
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Cited by 5 (0 self)
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Abstract. Graph-based clustering methods are successfully applied to computer vision and machine learning problems. In this paper we demonstrate how to introduce a-priori knowledge on class membership in a systematic and principled way: starting from a convex relaxation of the graph
Tiny a priori knowledge solves the interior problem
- in computed tomography,” Physics in Medicine and Biology
, 2008
"... interior problem in computed tomography is unique if a tiny a priori knowledge on the object f(x, y) is available in the form that f(x, y) is known on a small region located inside the region of interest. Furthermore, we advance the uniqueness result to obtain more general uniqueness results which c ..."
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Cited by 13 (0 self)
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interior problem in computed tomography is unique if a tiny a priori knowledge on the object f(x, y) is available in the form that f(x, y) is known on a small region located inside the region of interest. Furthermore, we advance the uniqueness result to obtain more general uniqueness results which
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