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Alexandra Deschamps-Sonsino we are very confident about our decision. Applying this update rules, an incrementally better classifier is obtained without any user interaction. Moreover, an already trained classifier can be retrained online and can therefore easily be adapted to a completely different scene. We demonstrate the

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Alexandra Deschamps-Sonsino reviewing and discussing a variety of related studies, we present two methods in detail, namely, the so called “translation approach” which translates the visual structures to semantic descriptors using the idea of statistical machine translation techniques, and another approach which finds the den

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Alexandra Deschamps-Sonsino we are very confident about our decision. Applying this update rules, an incrementally better classifier is obtained without any user interaction. Moreover, an already trained classifier can be retrained online and can therefore easily be adapted to a completely different scene. We demonstrate the

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Alexandra Deschamps-Sonsinother the network maintains certain structural features; specifically, we measure how well the change in structural features correlates with the change in the parameters, and (3) the reliability in the context of message spreading, that is how the time taken to broadcast a message changes as edges ar

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Alexandra Deschamps-Sonsinoto learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in this book makes it easy for someone to connect the dots...What You‘ll Learn .Use the model building process fl

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ers) has changed beyond recognition. .Science fiction writing, the entertainment industry, art, and modern interior design and architecture movements have also contributed to defining our aspirations around a f978-1-4842-3362-7978-1-4842-3363-4

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Cheaper, Embedded and Invisible,, in short, a lack of imagination around the home space in computing circles that is a reflection not only of the people working on these new advancements but of their biases and preconceived ideas of what happens at home.