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[Other resource] audiodma DL : 0: DSP音频处理示例The benefits of real-time analysis provided by DSP/BIOS are often required in programs that were engineered without it. When the program is not built from the ground up using the DSP/BIOS kernel and real-time analysis features, the lack of familiarity prevents engineers from reaping the benefits of this very powerful tool set, especially in the final stage of development when real-time analysis is needed most. This application note provides an example of the necessary steps required to utilize DSP/BIOS features in order to bring these solutions to bear in an existing application.-DSP audio processing examples The benefits of real-time Analytics sis provided by DSP / BIOS are often required in p rograms that were engineered without it. When t he program is not built from the ground up using t he DSP / BIOS kernel and real-time analysis feat ures. the lack of familiarity prevents engineers fro m reaping the benefits of this very powerful too l set, especially in the final stage of development wh en real-time analysis is needed most. This APPLIS ication note provides an example of the necessa ry steps required to utilize DSP / BIOS features in order to bring these solutions to bear in an ex IOTWS application.
Date : 2008-10-13 Size : 5.89kb User : 小挥
[Other resource] Herbrich-Learning-Kernel-Classifiers-Theory-and-Al DL : 0: Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.-Learning Kernel Classifiers : Theory and Algorithms. Introduction This chapter introduces the gene the acidic problem of machine learning and how it relat es to statistical inference. 1.1 The Learning P roblem and (Statistical) It was only inference a few years after the introduction of the first c omputer that one of man's greatest dreams seeme d to be realizable-artificial intelligence. B earing in mind that in the early days the most pow erful computers had much less computational po wer than a cell phone today, it comes as no surprise that much theoretical're search on the potential of machines' capabilit ies to learn took place at this time. This become 's a computational problem as soon as the dataset gets larger than a few hundred examples.
Date : 2008-10-13 Size : 2.42mb User : google2000