We compare two probabilistic approaches to neural networks - the first one based on the mixtures of product components and the second one using the mixtures of dependence-tree distributions. the product mixture models...
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ISBN:
(纸本)9789897580543
We compare two probabilistic approaches to neural networks - the first one based on the mixtures of product components and the second one using the mixtures of dependence-tree distributions. the product mixture models can be efficiently estimated from data by means of EM algorithm and have some practically important properties. However, in some cases the simplicity of product components could appear too restrictive and a natural idea is to use a more complex mixture of dependence-tree distributions. By considering the concept of dependence tree we can explicitly describe the statistical relationships between pairs of variables at the level of individual components and therefore the approximation power of the resulting mixture may essentially increase. Nonetheless, in application to classification of numerals we have found that both models perform comparably and the contribution of the dependence-tree structures decreases in the course of EM iterations. thus the optimal estimate of the dependence-tree mixture tends to converge to a simple product mixture model. Regardless of computational aspects, the dependence-tree mixtures could help to clarify the role of dendritic branching in the highly selective excitability of neurons.
this paper presents a novel eficient Markov Chain Monte Carlo (MCMC) method for License Plate (LP) localization. the proposed method formulates the LP image feature and prior knowledge into a unified Bayesian framewor...
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ISBN:
(纸本)9781450328104
this paper presents a novel eficient Markov Chain Monte Carlo (MCMC) method for License Plate (LP) localization. the proposed method formulates the LP image feature and prior knowledge into a unified Bayesian framework. then the localization problem is derived as a maximizing-a-posterior (MAP) problem, which integrates color, edge and character feature of LP. We propose an eficient MCMC method, taking integrated local geometrical likelihood as proposal probability to make the inference feasible. the experimental results on real dataset are very promising in terms of detection rate and localization accuracy. Copyright 2014 ACM.
Online group identification is a challenging task, due to the inherent dynamic nature of groups. In this paper, a novel framework is proposed that combines the individual trajectories produced by a tracker along with ...
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Cold start recommendation is a challenging but crucial problem for recommender systems. Preference Elicitation, as a commonly used approach to address the problem, solicits preference of cold user by interviewing them...
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ISBN:
(纸本)9781450328104
Cold start recommendation is a challenging but crucial problem for recommender systems. Preference Elicitation, as a commonly used approach to address the problem, solicits preference of cold user by interviewing them with some elaborately selected items. How to select minimum items to reflect user preference as much as possible is the essential goal of preference elicitation. In this paper, we propose a novel Structured Sparse Representative Selection(SSRS) model to select a sparse set of items based on their ability of representation. Moreover, a 2;1-norm is utilized on both loss function and regularization to make the model insensitive to outliers and avoid selecting redundant queries respectively. Empirical results on benchmark movie rating datasets Movielens and Flixster verify the promising performance of our proposed preference elicitation method for cold start recommendation. Copyright 2014 ACM.
Modern programming languages and operating systems use heap memory that allows allocation and deallocation of memory to be decoupled, so they don't follow a stack discipline. Axelsen and Glück have presented ...
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We propose a biometric method for identifying athletes based on information extracted from the gait style and the electrocardiographic (ECG) waveform. the required signals are recorded within a non-clinical acquisitio...
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ISBN:
(纸本)9783662444856;9783662444849
We propose a biometric method for identifying athletes based on information extracted from the gait style and the electrocardiographic (ECG) waveform. the required signals are recorded within a non-clinical acquisition setup using a wireless body sensor attached to a chest strap with integrated textile electrodes. Our method combines both sources of information to allow identification despite severe intra-subjects variations in the gait patterns (walking and jogging) and motion related artefacts in the ECG patterns. For identification we use features extracted in time and frequency domain and a standard classifier. Within a treadmill experiment with 22 subjects we obtained an accuracy of 98.1% for velocities from 3 to 9 km/h. On a second data set consisting of 9 subjects and two sessions of recording, our method achieved 93.8% despite variations in the patterns due to reapplying the body sensor and an increased velocity (up to 11 km/h).
Breast cancer continues to be a significant health problem in the world. the most familiar breast anomalies types are mass and microcalcification. However Automatic methods for detecting these abnormalities can identi...
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Breast cancer continues to be a significant health problem in the world. the most familiar breast anomalies types are mass and microcalcification. However Automatic methods for detecting these abnormalities can identify breast cancer at an early stage. In this paper, we propose a marker-controlled watershed algorithm to locate breast masses. the preprocessing step has been introduced to remove all undesirable areas from mammogram. Foreground and background markers are then selected in order to apply a watershed segmentation algorithm that identifies the location of tumor region in mammogram. the proposed method was successful to segment mass anomalies. It has been tested on publicly available Mammographic Image Analysis Society (MIAS) database and it has achieved an overall mass detection rate of 90.83% and an area Az of 0.913 under the receiver operating characteristic curve ROC for mass segmentation.
Classical process discovery approaches usually investigate logs generated by processes in order to mine and discovery corresponding process schemas. When the collaboration processes case is addressed, such approaches ...
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Classical process discovery approaches usually investigate logs generated by processes in order to mine and discovery corresponding process schemas. When the collaboration processes case is addressed, such approaches turn to be poorly effective, due to the fact that: (i) logs of collaboration processes are usually stored in heterogeneous data storages which also expose different data types; (ii) it is not easy and direct to derive a common analysis model from such logs. As a consequence, classical methodologies usually fail. In order to fulfill this gap, in this paper we describe a composite methodology that combines semantics-based techniques and multidimensional analysis paradigms to support effective and efficient collaboration process discovery from log data.
OCR errors hurt retrieval performance to a great extent. Research has been done on modelling and correction of OCR errors. However, most of the existing systems use language dependent resources or training texts for s...
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