Towards more efficient machine learning algorithms: theory and practice

迈向更高效的机器学习算法：理论与实践

• 批准号: RGPIN-2016-05942
• 负责人: Marchand, Mario
• 金额: $ 2.77万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616003
• 关键词: Towards; more; efficient; machine; learning

Machine learning is concerned with the development of intelligent computer systems that are able to learn and generalize from collected data. This ability is required for many important tasks that are just too complex to be explicitly programmed by humans such as recognizing voice directives on smart phones, or recognizing faces in images, or displaying the best results from a query. The best computer systems that achieve these tasks have in common the fact that their ability has been acquired by running learning algorithms on vast amounts of data. Many of these tasks are now so vital to our economy that machine learning-based technology is now commonplace. However, that technology needs to be greatly improved. Indeed, credit cards are too frequently being blocked, we are still receiving too many undesirable emails, and automatic speech recognition is still not satisfactory. Building more powerful hardware is just part of the solution as we must also find the most efficient learning algorithms. Consequently, the proposed research program aims at addressing the important problem of how to improve and modify existing learning algorithms such that they yield better predictors while using a minimal (or, at least, an acceptable) amount of resources. To meet this objective we plan to approach challenging machine-learning problems from both a theoretical and a practical perspective. Theoretical analysis is needed because we want to find efficient learning algorithms with provable guarantees. More specifically, we plan, in the short term, to improve the existing learning algorithms for structured output prediction and domain adaptation. These are currently two challenging machine-learning problems that model situations often encountered in practice but for which we do not have yet satisfactory learning algorithms. We also plan to find the most efficient boosting-type algorithms for learning from large-scale data sets. Finally, in the long term, we plan to expand the set covering machine and the decision list machine for predicting phenotypes from genomic data. These are learning algorithms (that we have proposed in the past) that produce uncharacteristically sparse predictors that are easy to interpret. Hence, these predictors can, in principle, help us to uncover the genomic cause of a phenotype. The impressive results we have obtained recently on the prediction of antibiotic resistance from bacterial genomes motivate us to consider, in the short term, the more ambitious problem of predicting cancer types from human sequences, which could be DNA, RNA, or protein sequences. It is thus expected that this research program will yield provably efficient learning methods for different machine learning-based applications that, in turn, will improve our quality of life. Moreover, it is expected that this research program will deliver the equivalent of two M.Sc graduates and four Ph.D graduates.

机器学习与能够从收集的数据中学习和推广的智能计算机系统的开发有关。对于许多重要的任务来说，需要这种功能，这些任务太复杂而无法由人类明确编程，例如在智能手机上识别语音指令，或者在图像中识别面孔，或者显示查询中的最佳结果。实现这些任务的最佳计算机系统通常是通过在大量数据上运行学习算法来获得其能力的事实。这些任务中的许多人现在对我们的经济至关重要，以至于基于机器学习的技术现在很普遍。但是，该技术需要大大改进。确实，信用卡太常见了，我们仍然收到太多不良电子邮件，并且自动语音识别仍然不满意。建立更强大的硬件只是解决方案的一部分，因为我们还必须找到最有效的学习算法。因此，拟议的研究计划旨在解决如何改善和修改现有学习算法的重要问题，以便它们在使用最少（或至少是可接受的）资源的同时产生更好的预测因子。为了满足这一目标，我们计划从理论和实际角度来解决具有挑战性的机器学习问题。需要理论分析，因为我们希望找到具有可证明保证的有效学习算法。更具体地说，我们计划在短期内改善用于结构化输出预测和域适应性的现有学习算法。这些目前是两个具有挑战性的机器学习问题，这些问题在实践中经常遇到，但我们尚未具有令人满意的学习算法。我们还计划找到用于从大规模数据集中学习的最有效的提升类型算法。最后，从长远来看，我们计划扩展设置覆盖机和决策列表机，以预测基因组数据的表型。这些是学习算法（过去我们提出的），产生了易于解释的非特征稀疏预测指标。因此，这些预测因子原则上可以帮助我们发现表型的基因组原因。我们最近在细菌基因组中预测抗生素耐药性的令人印象深刻的结果激发了我们在短期内考虑从人类序列预测癌症类型的更雄心勃勃的问题，即可能是DNA，RNA或蛋白质序列。因此，预计该研究计划将为不同的基于机器学习的应用程序产生可靠的有效学习方法，从而改善我们的生活质量。此外，预计该研究计划将提供相当于两名M.SC毕业生和四个博士学位毕业生。