Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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13 Apr 2009
TL;DR: A new technique for transient-based identification of CC2420 wireless sensor nodes is proposed and it is shown that it enables reliable and accurate sensor node recognition with an Equal Error Rate as low as 0.0024 (0.24%).
Abstract: Identification of wireless sensor nodes based on the characteristics of their radio transmissions can provide an additional layer of security in all-wireless multi-hop sensor networks. Reliable identification can be means for the detection and/or prevention of wormhole, Sybil and replication attacks, and can complement cryptographic message authentication protocols. In this paper, we investigate the feasibility of transient-based identification of CC2420 wireless sensor nodes. We propose a new technique for transient-based identification and show that it enables reliable and accurate sensor node recognition with an Equal Error Rate as low as 0.0024 (0.24%). We investigate the performance of our technique in terms of parameters such as distance, antenna polarization and voltage and analyze how these parameters affect the recognition accuracy. Finally, we study the feasibility of certain types of impersonation attacks on the proposed technique.
215 citations
TL;DR: This review first introduces the concept of Big Data in different environments, then describes how modern statistical learning models can be used in practice on Big Datasets to extract relevant information and discusses the strengths of using statistical learning in psychiatric studies.
Abstract: Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may be further complicated by missing data for some subjects and variables that are highly correlated. Statistical learning-based models are a natural extension of classical statistical approaches but provide more effective methods to analyse very large datasets. In addition, the predictive capability of such models promises to be useful in developing decision support systems. That is, methods that can be introduced to clinical settings and guide, for example, diagnosis classification or personalized treatment. In this review, we aim to outline the potential benefits of statistical learning methods in clinical research. We first introduce the concept of Big Data in different environments. We then describe how modern statistical learning models can be used in practice on Big Datasets to extract relevant information. Finally, we discuss the strengths of using statistical learning in psychiatric studies, from both research and practical clinical points of view.
215 citations
Cites background from "Pattern Recognition and Machine Lea..."
...Other approaches include pattern recognition, a branch of ML focused on the recognition of patterns and regularities in data (Bishop, 2006) and data mining, the process of exploring data in search of consistent patterns and/or systematic relationships between variables (Hand et al....
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...Other approaches include pattern recognition, a branch of ML focused on the recognition of patterns and regularities in data (Bishop, 2006) and data mining, the process of exploring data in search of consistent patterns and/or systematic relationships between variables (Hand et al. 2001)....
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13 Jun 2010
TL;DR: The experiments suggest a positive answer to the question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images?
Abstract: Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance.
214 citations
Cites background from "Pattern Recognition and Machine Lea..."
...Having sufficient variability in the sets of examples and counterexamples is decisive to train classifiers able to generalize properly [2]....
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20 Sep 2010
TL;DR: The possibility of using mobile phone sensors and opportunistic user-intersections to develop an electronic escort service, which design and implement Escort, a system that guides a user to the vicinity of a desired person in a public place.
Abstract: Finding a person in a public place, such as in a library, conference hotel, or shopping mall, can be difficult. The difficulty arises from not knowing where the person may be at that time; even if known, navigating through an unfamiliar place may be frustrating. Maps and floor plans help in some occasions, but such maps may not be always handy. In a small scale poll, 80% of users responded that the ideal solution would be "to have an escort walk me to the desired person". This paper identifies the possibility of using mobile phone sensors and opportunistic user-intersections to develop an electronic escort service. By periodically learning the walking trails of different individuals, as well as how they encounter each other in space-time, a route can be computed between any pair of persons. The problem bears resemblance to routing packets in delay tolerant networks, however, its application in the context of human localization raises distinct research challenges. We design and implement Escort, a system that guides a user to the vicinity of a desired person in a public place. We only use an audio beacon, randomly placed in the building, to enable a reference frame. We do not rely on GPS, WiFi, or war-driving to locate a person - the Escort user only needs to follow an arrow displayed on the phone. Evaluation results from experiments in parking lots and university buildings show that, on average, the user is brought to within 8m of the destination. We believe this is an encouraging result, opening new possibilities in mobile, social localization.
214 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Then, threedimensional Gaussian distributions [5] are fitted to the upper and lower subregions using maximum likelihood estimation (MLE)....
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TL;DR: A system that possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players, and proposes a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip.
Abstract: Tracking and identifying players in sports videos filmed with a single pan-tilt-zoom camera has many applications, but it is also a challenging problem. This paper introduces a system that tackles this difficult task. The system possesses the ability to detect and track multiple players, estimates the homography between video frames and the court, and identifies the players. The identification system combines three weak visual cues, and exploits both temporal and mutual exclusion constraints in a Conditional Random Field (CRF). In addition, we propose a novel Linear Programming (LP) Relaxation algorithm for predicting the best player identification in a video clip. In order to reduce the number of labeled training data required to learn the identification system, we make use of weakly supervised learning with the assistance of play-by-play texts. Experiments show promising results in tracking, homography estimation, and identification. Moreover, weakly supervised learning with play-by-play texts greatly reduces the number of labeled training examples required. The identification system can achieve similar accuracies by using merely 200 labels in weakly supervised learning, while a strongly supervised approach needs a least 20,000 labels.
213 citations