Results 281 to 290 of about 241,041 (317)
Some of the next articles are maybe not open access.
2017
Die Grundlagen der Hidden-Markov-Modelle (HMM) werden in diesem Kapitel behandelt. Dazu gehoren insbesondere die Algorithmen, welche die grundlegenden Probleme der HMM losen: Forward-Algorithmus, Viterbi-Algorithmus und Baum-Welch-Algorithmus. Das Trellis-Diagramm wird eingesetzt um diese Algorithmen anschaulich zu erklaren.
Beat Pfister, Tobias Kaufmann
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Die Grundlagen der Hidden-Markov-Modelle (HMM) werden in diesem Kapitel behandelt. Dazu gehoren insbesondere die Algorithmen, welche die grundlegenden Probleme der HMM losen: Forward-Algorithmus, Viterbi-Algorithmus und Baum-Welch-Algorithmus. Das Trellis-Diagramm wird eingesetzt um diese Algorithmen anschaulich zu erklaren.
Beat Pfister, Tobias Kaufmann
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2014
In the field of pattern recognition, signals are frequently thought of as the product of a statistical generation process. The primary goal of analyzing these signals is to model their statistical properties as exactly as possible. However, the model to be determined should not only replicate the generation of certain data but also deliver useful ...
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In the field of pattern recognition, signals are frequently thought of as the product of a statistical generation process. The primary goal of analyzing these signals is to model their statistical properties as exactly as possible. However, the model to be determined should not only replicate the generation of certain data but also deliver useful ...
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2003
Im Bereich der Mustererkennung betrachtet man Signale haufig als das Produkt statistisch agierender Quellen. Das Ziel der Signalanalyse ist es daher, die statistischen Eigenschaften dieser angenommenen Signalquellen moglichst genau zu modellieren. Als Basis der Modellbildung stehen dabei lediglich die beobachteten Beispieldaten sowie einschrankende ...
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Im Bereich der Mustererkennung betrachtet man Signale haufig als das Produkt statistisch agierender Quellen. Das Ziel der Signalanalyse ist es daher, die statistischen Eigenschaften dieser angenommenen Signalquellen moglichst genau zu modellieren. Als Basis der Modellbildung stehen dabei lediglich die beobachteten Beispieldaten sowie einschrankende ...
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2015
Markov chains and hidden Markov models (HMMs) are particular types of PGMs that represent dynamic processes. After a brief introduction to Markov chains, this chapter focuses on hidden Markov models. The algorithms for solving the basic problems: evaluation, optimal sequence, and parameter learning are presented.
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Markov chains and hidden Markov models (HMMs) are particular types of PGMs that represent dynamic processes. After a brief introduction to Markov chains, this chapter focuses on hidden Markov models. The algorithms for solving the basic problems: evaluation, optimal sequence, and parameter learning are presented.
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
exaly
2019
There are many situations where one must work with sequences. Here is a simple, and classical, example. We see a sequence of words, but the last word is missing. I will use the sequence “I had a glass of red wine with my grilled xxxx.” What is the best guess for the missing word?
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There are many situations where one must work with sequences. Here is a simple, and classical, example. We see a sequence of words, but the last word is missing. I will use the sequence “I had a glass of red wine with my grilled xxxx.” What is the best guess for the missing word?
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The biofilm life cycle: expanding the conceptual model of biofilm formation
Nature Reviews Microbiology, 2022Karin Sauer +2 more
exaly