1,721,706 research outputs found
SHIP LOGISTICS AND MANAGEMENT
Full text linkSeminario sulle tendenze della gestione della nave e della logistica navale diretto dall’ ing. Luigi Vitiello
Comitato tecnico-scientifico: ing. Luigi Vitiello, Prof. Carlo Bertorello (DII), ing. Dario Bocchetti (Grimaldi S.p.A.), C.V. Claudio Campana (MMI), ing. Umberto d’Amato, Prof. Ernesto Fasano (DII), Ing. Giuseppe Longobardi (Augustea Technoservices Ltd)
El límite del lenguaje : La filosofía de Heidegger como teoría de la enunciación
Full text linkResumen temporalmente no disponible. La presente obra no cuenta con resumen provisto por el autor.Fil: Bertorello, Mario Adrián. Universidad de Buenos Aires. Facultad de Filosofía y Letras
El límite del lenguaje : La filosofía de Heidegger como teoría de la enunciación
Full text linkResumen temporalmente no disponible. La presente obra no cuenta con resumen provisto por el autor.Fil: Bertorello, Mario Adrián. Universidad de Buenos Aires. Facultad de Filosofía y Letras
Fossati F., Robustelli F., Schito P., Bertorello C., “Experimental and numerical assessment of high speed small craft aerodynamics”, HSMV2014, Naples (Italy), October 2014
No full textData for: 3401653
No full textThe Courchevel environment is hereby published to ease the development of streaming machine learning algorithms. In first solving this problem, a rapid reinforcement learning algorithm was invented. A simple transformation is added to the Bellman equation, a principal pillar of AI, particularly for solving Markov Decision Problems. By adding stochasticity to Bellman, sustained Reward-Per-Episode gains of an order of magnitude are validated, for environments where the reward function is structurally anticipated to be multi-modal. Courchevel as a decision problem, a first solution, and the Biased Bellman innovation are revealed -- with accompanying data. For ease of discussion, Courchevel's dynamics are described in military terms
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No full textIn important prediction scenarios, data-sets are naturally imbalanced, for instance in cancer detection: a small minority of people may exhibit the disease. This poses a significant classification challenge to machine learning algorithms. Data imbalance can cause lower performance for the class of interest, e.g. classifying with high precision that the person has cancer. When training data is abundant, a possible approach is to down-sample the majority class, thus restoring balance. Another prevalent approach is weighting, accelerating learning for minority class training examples. Synthesis is a major alternative, producing examples of the minority class, adding them to the training set to overcome the class imbalance. The Synthetic Minority Over-sampling Technique, SMOTE is widely applied, but it was not developed for image data. Rather, this research applies Generative Adversarial Networks, which generate image examples drawn from the minority class distribution. The novel SMate approach leverages GAN minority-class image generators, which benefit from Transfer Learning from majority-class image generators. Consequently, SMate outperforms SMOTE for imbalanced image data-sets
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