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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TL;DR: The proposed wavelet-based method has the advantages of fast execution time and low memory requirements and is potentially well-suited for real-time implementation with onboard UGS systems.
Abstract: Unattended ground sensors (UGS) are widely used to monitor human activities, such as pedestrian motion and detection of intruders in a secure region. Efficacy of UGS systems is often limited by high false alarm rates, possibly due to inadequacies of the underlying algorithms and limitations of onboard computation. In this regard, this paper presents a wavelet-based method for target detection and classification. The proposed method has been validated on data sets of seismic and passive infrared sensors for target detection and classification, as well as for payload and movement type identification of the targets. The proposed method has the advantages of fast execution time and low memory requirements and is potentially well-suited for real-time implementation with onboard UGS systems.
135 citations
TL;DR: Novel PV power generation and load power consumption prediction algorithms are presented, which are specifically designed for a residential storage controller and the optimal size of the energy storage module is determined so as to minimize the break-even time of the initial investment in the PV and storage systems.
Abstract: Integration of residential-level photovoltaic (PV) power generation and energy storage systems into the smart grid will provide a better way of utilizing renewable power. With dynamic energy pricing models, consumers can use PV-based generation and controllable storage devices for peak shaving on their power demand profile from the grid, and thereby, minimize their electric bill cost. The residential storage controller should possess the ability of forecasting future PV power generation as well as the power consumption profile of the household for better performance. In this paper, novel PV power generation and load power consumption prediction algorithms are presented, which are specifically designed for a residential storage controller. Furthermore, to perform effective storage control based on these predictions, the proposed storage control algorithm is separated into two tiers: the global control tier and the local control tier. The former is performed at decision epochs of a billing period (a month) to globally “plan” the future discharging/charging schemes of the storage system, whereas the latter one is performed more frequently as system operates to dynamically revise the storage control policy in response to the difference between predicted and actual power generation and consumption profiles. The global tier is formulated and solved as a convex optimization problem at each decision epoch, whereas the local tier is analytically solved. Finally, the optimal size of the energy storage module is determined so as to minimize the break-even time of the initial investment in the PV and storage systems.
135 citations
Cites background from "Pattern Recognition and Machine Lea..."
...The proposed intra-day refinement process is crucial since the characteristics of the load power consumption and PV power generation profiles are required to be more accurate for the 1st LP, HP, and 2nd LP periods compared to the 1st OP period....
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...Hence during the 1st OP period, the storage system is being charged, instead of being discharged as in the following 1st LP, the HP, and the 2nd LP periods....
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...The output power levels of the PV and storage systems at the th time slot are denoted by and , respectively, where can be positive (discharging the storage), negative (charging the storage), or zero....
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...We have where is the storage energy when fully charged, and , since the storage is being discharged in the 1st LP, the HP, and the 2nd LP period....
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...For notation simplicity, we denote the 1st OP, 1st LP, HP, 2nd LP, and 2nd OP price periods of a day as the 1st, 2nd, 3rd, 4th, and 5th price periods of that day....
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20 Jun 2009
TL;DR: This work investigates the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet, and achieves a word recognition accuracy of 98.9% on a dataset of 1,000 low quality webcam videos of 100 words.
Abstract: We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding each other, and contains signs which are ambiguous from the observer's viewpoint. The main contributions of our work include: (i) recognition based on hand shape alone, not requiring motion cues; (ii) robust visual features for hand shape recognition; (iii) scalability to large lexicon recognition with no re-training. We report results on a dataset of 1,000 low quality webcam videos of 100 words. The proposed method achieves a word recognition accuracy of 98.9%.
134 citations
Cites background from "Pattern Recognition and Machine Lea..."
...The parameters w are set by maximum likelihood on the training data (see [1])....
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...A good compromise between accuracy and computational efficiency was found to be multi-class logistic regression (see [1])....
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TL;DR: Describing methods for segmentation, feature extraction, selection, and dimensionality reduction, as well as clustering, outlier detection, and classification of data, are provided.
Abstract: With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.
134 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...For both forms of cross validation, the mean of the validations across the repetitions is used as the validation metric (Bishop, 2006)....
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TL;DR: A regularization approach to learning the relationships between tasks in multitask learning that can also describe negative task correlation and identify outlier tasks based on the same underlying principle is proposed.
Abstract: Multitask learning is a learning paradigm that seeks to improve the generalization performance of a learning task with the help of some other related tasks. In this article, we propose a regularization approach to learning the relationships between tasks in multitask learning. This approach can be viewed as a novel generalization of the regularized formulation for single-task learning. Besides modeling positive task correlation, our approach—multitask relationship learning (MTRL)—can also describe negative task correlation and identify outlier tasks based on the same underlying principle. By utilizing a matrix-variate normal distribution as a prior on the model parameters of all tasks, our MTRL method has a jointly convex objective function. For efficiency, we use an alternating method to learn the optimal model parameters for each task as well as the relationships between tasks. We study MTRL in the symmetric multitask learning setting and then generalize it to the asymmetric setting as well. We also discuss some variants of the regularization approach to demonstrate the use of other matrix-variate priors for learning task relationships. Moreover, to gain more insight into our model, we also study the relationships between MTRL and some existing multitask learning methods. Experiments conducted on a toy problem as well as several benchmark datasets demonstrate the effectiveness of MTRL as well as its high interpretability revealed by the task covariance matrix.
134 citations