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Context-Sensitive Semantic Smoothing for the Language Modeling ApProach to Genomic IR(上下文敏感的語義平滑的語言建模方法)
標簽： Context-Sensitive Smoothing ApProach Semantic
上傳時間： 2015-11-04
上傳用戶：pkkkkp
An AHB system is made of masters slaves and interconnections. A general ApProach to include all possible "muxed" implementation of multi layered AHB systems and arbitrated AHB ones can be thought as an acyclic graph where every source node is a master, every destination node is a slave and every internal node is an arbiter there must be one and only one arc exiting a master and one or more entering a slave (single slave verus multi-slave or arbitrated slave) an arbiter can have as many input and output connections as needed. A bridge is a special node that collapses one or more slave nodes and a master node in a new "complex" node.
標簽： interconnections ApProach general include
上傳時間： 2015-12-12
上傳用戶：lyy1234
DAGON ApProach Object of this exercise: Given a subject graph and a set of pattern graph in canonical representation (2-input NAND and INV), implement the second step of DAGON ApProach. (Both the subject graph and the pattern graphs are trees.)
標簽： graph ApProach exercise subject
上傳時間： 2014-01-19
上傳用戶：stampede
We propose a novel ApProach for head tracking, which combines particle filters with Isomap. The particle filter works on the low-dimensional embedding of training images. It indexes into the Isomap with its state variables to find the closest template for each particle. The most weighted particle approximates the location of head. We develop a synthetic video sequence to test our technique. The results we get show that the tracker tracks the head which changes position, poses and lighting conditions.
標簽： ApProach combines particle tracking
上傳時間： 2016-01-02
上傳用戶：yy541071797
Degraded Image Analysis:An Invariant ApProach一篇經典的關于不變矩的文章。
標簽： Invariant Degraded Analysis ApProach
上傳時間： 2016-01-05
上傳用戶：ddddddos
神經網路學習分類..Neural Fault Classifier for Transmission Line Protection A Modular ApProach
標簽： Transmission Classifier Protection ApProach
上傳時間： 2016-01-16
上傳用戶：shizhanincc
Computer Architecture--A Quantitative ApProach.pdf
標簽： Architecture Quantitative Computer ApProach
上傳時間： 2014-01-17
上傳用戶：努力努力再努力
Boosting is a meta-learning ApProach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This ApProach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標簽： meta-learning classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
finite difference method with Crank-Nikolson ApProach(SOR) for American call option
標簽： Crank-Nikolson difference American ApProach
上傳時間： 2016-01-30
上傳用戶：從此走出陰霾
Level Crossing Rate in Terms of the Characteristic Function: A New ApProach for Calculating the Fading Rate in Diversity Systems
標簽： Characteristic Calculating the Crossing
上傳時間： 2016-02-19
上傳用戶：shinesyh
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