data in raw or processed form can send a request down the tree. When the request reaches the node 6 that has (at least part of) the data, it can decide whether the requested access is permitted. If yes …

Microsoft Word - Nantasenamat_24112011_proof.doc

for assessing the prevalence of metabolic syn- drome in Thai population. Acta Diabetol 2010b;47:S91-6. Worachartcheewan A, Nantasenamat C, Isarankura-Na-Ayudhya C, Prachayasittikul S, Prachayasittikul V …

Science, Technology & Innovation Studies Vol. 6, No. 1, August 2010 ISSN: 1861-3675 STI Studies STI Studies www.sti-studies.de Social robustness as analytical tool or normative standard? A comment on Monika …

final.dvi

Four points that cannot be shattered by sinx despite in nite VC dimension 0.2 0.4 0.6 0.8 1 1.2 1.4 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 V C C on fid en ce h / l = VC Dimension / Sample Size Figure VC con [...] equally well have chosen H to again be R and x p x x xx x x A 0.2 0.4 0.6 0.8 1 -1 -0.5 0 0.5 1 0 0.2 0.4 0.6 0.8 1 Figure Image in H of the square R under the mapping or H to be R and [...] of tested 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 100 200 300 400 500 600 700 800 900 1000 A ct ua l R is k : S V B ou nd : V C B ou nd Sigma Squared 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0 100 200 300 400 500 600 …

Entrenched in detachment: Professional values are the main constraint to accountability in the Unite

http://mediacouncil.org 4 http://www.newsombudsmen.org/regmem.htm David Domingo: Entrenched in detachment 6 the US could ever call someone else on their ethical practices or their lack of transparency seems absurd [...] lag behind, start-ups have an opportunity to “get in there and create this new kind of dynamic.” 1.6 The Future Ahead There may be some amount of technological determinism in the interviewees when they [...] a column on the issue criticizing the harsh reaction of Twitter users and bloggers to a minor error6. I prefer the TBD approach. “We have to acknowledge what is going on in the world outside, we have to …

Science, Technology & Innovation Studies Vol

practices in the field under study (Garfinkel and Wieder 1992: 182-184; Lynch 1993: 271-275).6 When these 6 Lynch links his methodology to the work of Winch (1958; 1974). Hirschauer (1994: 338-345) traces [...] ductors, which he had to abandon be- cause he could not understand any of the science. (ibid., note 6). The same approach can be assigned to Lynch (1982; 1985; 1993; 1994), who bases it on ethnomethodology’s [...] must start from scratch, and doing this with one’s own experi- ences is not the worst way to begin. 6 References Berger, Peter L., and Thomas Luckmann, 1967. The Social Construction of Real- ity. Harmondsworth: …

Vorlesung Maschinelles Lernen - Klassifikation und Regression: Lineare Modelle

[ŷ0])2 +ET [ŷ2 0] = V arT (y0) + (ET [ŷ0])2 (6) Damit folgt MSE(!x0) = ET [y0 ' ŷ0]2 = ET 3 y2 0 ' 2y0ŷ0 + ŷ2 0 4 (2) = ET [y2 0]' ET [2yŷ0] + ET [ŷ2 0] (6) = ET [y2 0]' ET [2y0ŷ0] + V arT (ŷ0) + (ET [...] gelernt, so liefert uns dieses Modell mit ŷ = f̂ (!x) für neue Daten !x $ X eine Vorhersage ŷ $ Y . 6 von 39 LS 8 Künstliche Intelligenz Fakultät für Informatik Technische Universität Dortmund Lineare Modelle …

2MLVbiasVariance.de.pdf

[ŷ0])2 +ET [ŷ2 0] = V arT (y0) + (ET [ŷ0])2 (6) Damit folgt MSE(!x0) = ET [y0 ' ŷ0]2 = ET 3 y2 0 ' 2y0ŷ0 + ŷ2 0 4 (2) = ET [y2 0]' ET [2yŷ0] + ET [ŷ2 0] (6) = ET [y2 0]' ET [2y0ŷ0] + V arT (ŷ0) + (ET [...] gelernt, so liefert uns dieses Modell mit ŷ = f̂ (!x) für neue Daten !x $ X eine Vorhersage ŷ $ Y . 6 von 39 LS 8 Künstliche Intelligenz Fakultät für Informatik Technische Universität Dortmund Lineare Modelle …

2MLVbiasVariance.de

[ŷ0])2 +ET [ŷ2 0] = V arT (ŷ0) + (ET [ŷ0])2 (6) Damit folgt MSE(!x0) = ET [y0 ' ŷ0]2 = ET 3 y2 0 ' 2y0ŷ0 + ŷ2 0 4 (2) = ET [y2 0]' ET [2y0ŷ0] + ET [ŷ2 0] (6) = ET [y2 0]' ET [2y0ŷ0] + V arT (ŷ0) + [...] gelernt, so liefert uns dieses Modell mit ŷ = f̂ (!x) für neue Daten !x $ X eine Vorhersage ŷ $ Y . 6 von 39 LS 8 Informatik Computergestützte Statistik Technische Universität Dortmund Lineare Modelle zur …

DeepLearning on FPGAs - Artifical Neuronal Networks: Image classification

708.100 weights Thus: Even for small images we need to learn a lot of weights DeepLearning on FPGAs 6 Image Representation Observation 1: Even smaller images need a lot of neurons width ≈ 256− 1920 height [...] 708.100 weights Thus: Even for small images we need to learn a lot of weights DeepLearning on FPGAs 6 Image Representation: Making images smaller Obviously: Images need to be smaller! Merge a r × r grid …