We discuss a novel framework for integrated sensing and resource management in a distributed intelligent sensor network with sensor nodes whose useful active lifetime is significantly shorter than the required lifetim...
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ISBN:
(纸本)9780780387355
We discuss a novel framework for integrated sensing and resource management in a distributed intelligent sensor network with sensor nodes whose useful active lifetime is significantly shorter than the required lifetime of the sensor system. Past sensor network research has focused on security and communication, but largely ignored the overall dynamic resource management issue of such distributed systems. Our contribution is in integrated control optimization and resource management algorithms to ensure proper functioning of distributed sensors in extremely limited bandwidth, power, and storage. In this paper, we present a novel genetic algorithm for real-time system control and resource management that handles multiple conflicting objectives and constraints in the distributed system. Our framework is suitable for dynamic environments where the desired system performance and resource usage changes dynamically while being constrained by limited amount of resources.
When combining classifiers in order to improve the classification accuracy, precise estimation of the reliability of each member classifier can be very beneficial. One approach for estimating how confident we can be i...
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ISBN:
(纸本)3540287574
When combining classifiers in order to improve the classification accuracy, precise estimation of the reliability of each member classifier can be very beneficial. One approach for estimating how confident we can be in the member classifiers' results being correct is to use specialized critics to evaluate the classifiers' performances. We introduce an adaptive, critic-based confidence evaluation scheme, where each critic can not only learn from the behavior of its respective classifier, but also strives to be robust with respect to changes in its classifier. This is accomplished via creating distribution models constructed from the classifier's stored output decisions, and weighting them in a manner that attempts to bring robustness toward changes in the classifier's behavior. Experiments with handwritten character classification showing promising results are presented to support the proposed approach.
Small-sample learning in image retrieval is a pertinent and interesting problem. Relevance feedback is an active area of research that seeks to find algorithms that are robust with only a small number of examples. Muc...
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Histopathological tissue analysis by microscopy is a process that is subjective, prone to inter- and intra-observer variation. This, along with the problems associated with verbalising visual elements of the diagnosti...
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ISBN:
(纸本)3540288333
Histopathological tissue analysis by microscopy is a process that is subjective, prone to inter- and intra-observer variation. This, along with the problems associated with verbalising visual elements of the diagnostic process, make learning the skill quite difficult. Training is long and largely relies on an "apprentice" model, where trainees learn the skill by witnessing an expert at work, Here we present the first findings of a longitudinal study of a group of histopathology trainees. By monitoring the progress of the trainees, we hope to be able to provide information that will improve training and assessment. In this paper we discuss the results of early data collection and analysis, from a web-based study of trainee classification accuracy and classification time.
This paper addresses the clustering problem of hidden dynamical systems behind observed multivariate sequences by assuming an interval-based temporal structure in the sequences. Hybrid dynamical systems that have tran...
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ISBN:
(纸本)3540287574
This paper addresses the clustering problem of hidden dynamical systems behind observed multivariate sequences by assuming an interval-based temporal structure in the sequences. Hybrid dynamical systems that have transition mechanisms between multiple linear dynamical systems have become common models to generate and analyze complex time-varying event. Although the system is a flexible model for human motion and behaviors, the parameter estimation problem of the system has a paradoxical nature: temporal segmentation and system identification should be solved simultaneously. The EM algorithm is a well-known method that solves this kind of paradoxical problem;however the method strongly depends on initial values and often converges to a local optimum. To overcome the problem, we propose a hierarchical clustering method of linear dynamical systems by constraining eigenvalues of the systems. Due to the constraints, the method enables parameter estimation of dynamical systems from a small amount of training data, and provides well-behaved initial parameters for the EM algorithm. Experimental results on simulated and real data show the method can organize hidden dynamical systems successfully.
The human immune system is a highly complex machinery tuned to recognize specific molecular patterns in order to distinguish self from non-self proteins. Specialized immune cells can recognize major histocompatibility...
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ISBN:
(纸本)3540287574
The human immune system is a highly complex machinery tuned to recognize specific molecular patterns in order to distinguish self from non-self proteins. Specialized immune cells can recognize major histocompatibility (MHC) molecules with bound protein fragments (peptides) on the surface of other cells. If these peptides originate from virus or cancer proteins, the immune cells can induce controlled cell death. In silico vaccine design typically starts with the identification of peptides that might induce an immune response as a first step. This is typically done by searching for specific amino acid patterns obtained from peptides known to be recognized by the immune system. We propose a new method for deriving decision rules based on the physiochemical properties of such peptides. The rulesets generated give insights into the underlying mechanism of MHC-peptide interaction. Furthermore, we show that these rulesets can be used for high accuracy prediction of MHC binding peptides.
Modeling relational data is an important problem for modern data analysis and machinelearning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects intera...
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According to the sizes of the attribute set and the information table, the information tables are categorized into three types of Rough Set problems, patternrecognition/machinelearning problems, and Statistical Mode...
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ISBN:
(纸本)3540286535
According to the sizes of the attribute set and the information table, the information tables are categorized into three types of Rough Set problems, patternrecognition/machinelearning problems, and Statistical Model Identification problems. In the first Rough Set situation, what we have seen is as follows: 1) The "granularity" should be taken so as to divide equally the unseen tuples out of the information table, 2) The traditional "Reduction" sense accords with the above insistence, and 3) The "stable" subsets of tuples, which are defined through a "Galois connection" between the subset and the corresponding attribute subset, may play an important role to capture some characteristics that can be read from the given information table. We show these with some illustrative examples.
In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present a measure of inference risk as a function of reconstruction precision and number of collud...
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The task of extracting knowledge from text is an important research problem for information processing and document understanding. Approaches to capture the semantics of picture objects in documents constitute subject...
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