کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
431862 | 688642 | 2013 | 9 صفحه PDF | دانلود رایگان |
کلمات کلیدی
1. مقدمه: یادگیری ماشینی موازی
2. رویکردهای یادگیری ماشین موازی عمومی
2.1. کاوش قواعد وابستگی و کاوش مجموعه اقلام پرتکرار
2.1.1 الگتوریمهای مهم و دیگر مشاهدات – ARM
2.2. دیگر رویکردهای یادگیری ماشینی
2.2.1. انتشارات مهم و مشارکتها
3. دادهکاوی، یادگیری ماشینی و تلاشهای مرتبط بر روی واحدهای پردازنده گرافیکی
3.1. مشاهدات اصلی
3.2. یادگیری ماشینی و تکنیکهای مرتبط بر روی واحدهای پردازنده گرافیکی: انتشارات مهم و مشارکتهایشان
4. دادهکاوی و یادگیری ماشینی با استفاده از تکنیک نگاشتکاهش
4.1. مشاهدات مهم
4.2. یادگیری ماشینی/کاوش دادههای موازی با استفاده از تکنیک نگاشتکاهش – انتشارات مهم و مشارکتهایشان
5. شکافهای حیاتی، مشکلات باز و زمینه برای تحقیق
6. نتیجهگیری
Literature has always witnessed efforts that make use of parallel algorithms / parallel architecture to improve performance; machine learning space is no exception. In fact, a considerable effort has gone into this area in the past fifteen years. Our report attempts to bring together and consolidate such attempts. It tracks the development in this area since the inception of the idea in 1995, identifies different phases during the time period 1995–2011 and marks important achievements. When it comes to performance enhancement, GPU platforms have carved a special niche for themselves. The strength of these platforms comes from the capability to speed up computations exponentially by way of parallel architecture / programming methods. While it is evident that computationally complex processes like image processing, gaming etc. stand to gain much from parallel architectures; studies suggest that general purpose tasks such as machine learning, graph traversal, and finite state machines are also identified as the parallel applications of the future. Map reduce is another important technique that has evolved during this period and as the literature has it, it has been proved to be an important aid in delivering performance of machine learning algorithms on GPUs. The report summarily presents the path of developments.
► Study on the literature available in the past decade and a half covering parallel machine learning approaches.
► Marks important contributions and discusses the salient or most outstanding differentiator of each contribution.
► Segregates the content under three heads and analyzes each head and states the observation trends found.
► Finds critical gaps, suggests future outlook, and summarizes the findings.
Journal: Journal of Parallel and Distributed Computing - Volume 73, Issue 3, March 2013, Pages 284–292