翻译机器学习日报.docx

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectorsProf. Kai Ming TingAbstract: Conventional wisdom in machine learning says that all algorithms are expected to follow the trajectory of a learning curve which is often colloquially referred to as‘more data the better’. We call this?‘the gravity of learning curve’, and it is assumed that no learning algorithms are?‘gravity-defiant’. Contrary to the conventional wisdom, this talk provides the theoretical analysis and the empirical evidence that nearest neighbour anomaly detectors are gravity-defiant algorithms.In the age of big data, the revelation and the knowledge about the gravity-defiant behaviour discovered have two impacts. First, the capacity provided by big data infrastructures would be overkill because the gravity-defiant algorithms that produce good performing models using small datasets can be executed comfortably in existing computing infrastructures. Second, it opens a whole new direction of research into different types of gravity-defiant algorithms which can achieve high performance with small sample size.在机器学习的传统思维里，所有的算法都会遵循一个学习曲线，可以简单地表述为数据越多，效果越好。我们将这个原理称之为学习曲线的固性，没有任何一种算法是例外。不同于传统思维，这篇报道从理论与和实验上证明了最近邻异常检测算法是一种反固性的算法。在大数据时代，由反固性特征带来的启示主要有两方面：首先，大数据的优势不复存在，因为在现有的计算结构下，反学习固性算法可以用少量的数据集实现更好的性能。第二，它开辟了一个新的方向，使得我们可以去创造更多反学习固性算法，使得我们能够用更小的数据集来实现更高的性能Brief Bio: After receiving his PhD from the University of Sydney, Kai Ming Ting had worked at the University of Waikato, Deakin University and MonashUniversity. He joins Federation University Australia since 2014. He had previously held visiting positions at Osaka University, Nanjing University, and Chinese University of Hong Kong. His current research interests are in the areas of mass estimation, anomaly detection, ensemble approaches, data streams, data mining and machine learning in general. He has served as a program committee co-chair for the Twelfth Pacific-Asia Conference on Knowledge Discovery and Data Mining