Half-space mass : a maximally robust and efficient data depth method
- Authors: Chen, Bo , Ting, Kaiming , Washio, Takashi , Haffari, Gholamreza
- Date: 2015
- Type: Text , Journal article
- Relation: Machine Learning Vol. 100, no. 2-3 (2015), p. 677-699
- Full Text: false
- Reviewed:
- Description: Data depth is a statistical method which models data distribution in terms of center-outward ranking rather than density or linear ranking. While there are a lot of academic interests, its applications are hampered by the lack of a method which is both robust and efficient. This paper introduces Half-Space Mass which is a significantly improved version of half-space data depth. Half-Space Mass is the only data depth method which is both robust and efficient, as far as we know. We also reveal four theoretical properties of Half-Space Mass: (i) its resultant mass distribution is concave regardless of the underlying density distribution, (ii) its maximum point is unique which can be considered as median, (iii) the median is maximally robust, and (iv) its estimation extends to a higher dimensional space in which the convex hull of the dataset occupies zero volume. We demonstrate the power of Half-Space Mass through its applications in two tasks. In anomaly detection, being a maximally robust location estimator leads directly to a robust anomaly detector that yields a better detection accuracy than half-space depth; and it runs orders of magnitude faster than L2 depth, an existing maximally robust location estimator. In clustering, the Half-Space Mass version of K-means overcomes three weaknesses of K-means.
- Description: Data depth is a statistical method which models data distribution in terms of center-outward ranking rather than density or linear ranking. While there are a lot of academic interests, its applications are hampered by the lack of a method which is both robust and efficient. This paper introduces
A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier
- Authors: Ghosh, Moumita , Ghosh, Ranadhir , Verma, Brijesh
- Date: 2004
- Type: Text , Journal article
- Relation: International Journal of Pattern Recognition and Artificial Intelligence Vol. 18, no. 7 (Nov 2004), p. 1267-1283
- Full Text: false
- Reviewed:
- Description: In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing- Author
- Description: C1
- Description: 2003000867
Search and tracking algorithms for swarms of robots: A survey
- Authors: Senanayake, Madhubhashi , Senthooran, Ilankaikaone , Barca, Jan , Chung, Hoam , Kamruzzaman, Joarder , Murshed, Manzur
- Date: 2016
- Type: Text , Journal article
- Relation: Robotics and Autonomous Systems Vol. 75, no. Part B (2016), p. 422-434
- Full Text: false
- Reviewed:
- Description: Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. © 2015 Elsevier B.V.
Mass estimation and its applications
- Authors: Ting, Kaiming , Zhou, Guangtong , Liu, Fei , Chuan, J. T. S.
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining p. 989-998
- Full Text: false
- Reviewed:
- Description: This paper introduces mass estimation--a base modelling mechanism in data mining. It provides the theoretical basis of mass and an efficient method to estimate mass. We show that it solves problems very effectively in tasks such as information retrieval, regression and anomaly detection. The models, which use mass in these three tasks, perform at least as good as and often better than a total of eight state-of-the-art methods in terms of task-specific performance measures. In addition, mass estimation has constant time and space complexities.
Commentary : A decomposition of the outlier detection problem into a set of supervised learning problems
- Authors: Zhu, Ye , Ting, Kaiming
- Date: 2016
- Type: Text , Journal article
- Relation: Machine Learning Vol. 105, no. 2 (2016), p. 301-304
- Full Text: false
- Reviewed:
- Description: This article discusses the material in relation to iForest (Liu et al. in ACM Trans Knowl Discov Data 6(1):3, 2012) reported in a recent Machine Learning Journal paper by Paulheim and Meusel (Mach Learn 100(2–3):509–531, 2015). It presents an empirical comparison result of iForest using the default parameter settings suggested by its creator (Liu et al. 2012) and iForest using the settings employed by Paulheim and Meusel (2015). This comparison has an impact on the conclusion made by Paulheim and Meusel (2015). © 2016, The Author(s).