Android 2: Grundlagen und Programmierung by Arno Becker PDF

By Arno Becker

ISBN-10: 3898646777

ISBN-13: 9783898646772

Java-erfahrene Leser lernen in diesem Buch, hochwertige software program für die Android-Plattform zu entwickeln. Im ersten Teil des Buches lernt der Leser zunächst die grundlegenden Konzepte und Elemente von Android kennen. Im anschließenden Praxisteil kann er anhand eines durchgängigen Beispiels Schritt für Schritt die Entwicklung einer mobilen Anwendung nachvollziehen. Der dritte Teil bereitet Entwickler auf den professionellen und sicheren Einsatz von Android in der Praxis vor.
Die 2. Auflage wurde komplett auf die Android-Version 2 aktualisiert und um neue Themen erweitert (Live Folder, Kamera- und Video-Unterstützung, SMS senden und empfangen).

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Extra info for Android 2: Grundlagen und Programmierung

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Thus, the network analyses the discrete functions f0 (n) = f (xn ), xn = x0 + nΔx. The weight distribution function of the first layer w1 (i) and the transfer function h1 (n) are of the form: ⎧ ⎨ −a f or i = 0 a f or i = 1 w1 (i) = (14) ⎩ 0 i = 0, i = 1 where parameter a > 0 ⎧ ⎨ h1 (e) = ⎩ 0 k(e+p) 2p k f or e < −p f or − p ≤ e ≤ p f or e > p (15) where p and k are parameters of positive values (figure 3a). The weight distribution function of the second layer w2 (i) is of the form: ⎧ ⎨ b f or i = 0 (16) w2 (i) = −b f or i = 1 ⎩ 0 i = 0, i = 1 where parameter b > 0 The parameters of the second layer transfer function fullfil the following relations: bk/3 ≤ pd < bk/2 and pg + pd = bk (pg , pd , k denote, respectively, threshold point, saturation point and value of saturation, figure 3b).

Such solutions can be even more effective than the obtained by means of traditional training [1],[2],[11],[15],[16] if only all TD are available at the beginning of the adaptation process. The complete information about all TD enables more precise tuning thanks to more global view on the TD what is a great advantage because construction of a mathematical model can take into account all specific information about all TD. This produces good generalization results of a NN constructed and adapted in this way [3],[7],[8],[13],[14].

C Springer-Verlag Berlin Heidelberg 2008 The Influence of Training Data Availability Time 29 time. In contrast, we look forward that the ANNs should be usually created and trained on a basis of a given TD acquired in the past. TD does usually not change during training process. The time-expensive training process necessary for adapting and tuning of NNNs can be replaced with other adaptation or construction algorithms that can compute a NN structure and its parameters more quickly. Such solutions can be even more effective than the obtained by means of traditional training [1],[2],[11],[15],[16] if only all TD are available at the beginning of the adaptation process.

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Android 2: Grundlagen und Programmierung by Arno Becker

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