Distant supervision (DS) aligns relations between name entities from a knowledge base (KB) with free text and automatically annotates the training corpus with relation mentions. One big challenge of DS is that the heu...
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ISBN:
(纸本)9783319265353;9783319265346
Distant supervision (DS) aligns relations between name entities from a knowledge base (KB) with free text and automatically annotates the training corpus with relation mentions. One big challenge of DS is that the heuristically generated relation labels usually tend to be noisy, when a pair of entity has multiple and/or incomplete relations in a KB. This paper proposes two ranking-based methods to reduce noise and select effective training data for multi-instance multi-label learning (MIML), one of the most popular learning paradigms for distantly supervised relation extraction. Through the proposed methods, training groups that are of low quality are excluded from the training data according to different ranking strategies. Experimental evaluation on the KBP dataset using state-of-the-art MIML algorithms in this community demonstrated that the proposed methods improved the performance significantly.
Base stations have been massively deployed nowadays to afford the explosive demand to infrastructure-based mobile networking services, including both cellular networks and commercial WiFi access points. To maintain hi...
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ISBN:
(纸本)9781450340731
Base stations have been massively deployed nowadays to afford the explosive demand to infrastructure-based mobile networking services, including both cellular networks and commercial WiFi access points. To maintain high service availability, backup battery groups are usually installed on base stations and serve as the only power source during power outages, which can be prevalent in rural areas or during severe weather conditions such as hurricanes or snow storms. Therefore, being able to understand and predict the battery group working condition is of immense technical and commercial importance as the first step towards a cost-effective battery maintenance on minimizing service interruptions. In this paper, we conduct a systematical analysis on a real world dataset collected from the battery groups installed on the base stations of China Mobile, with totally 1,550,032,984 records from July 28th, 2014 to February 17th, 2016. We find that the working condition degradation of a battery group may be accelerated under various situations and can cause premature failures on batteries in the group, which can hardly be captured by nowadays maintenance procedure and easily lead to a power-outage-triggered service interruption to a base station. To this end, we propose BatPro, a battery profiling framework, to precisely extract the features that cause the working condition degradation of the battery group. We formulate the prediction models for both battery voltage and lifetime and develop a series of solutions to yield accurate outputs. By real world trace-driven evaluations, we demonstrate that our BatPro approach can precisely predict the battery voltage and lifetime with the RMS error less than 0.01 v.
The multi-instancemulti-label (MIML) learning is a learning framework where each example is described by a bag of instances and corresponding to a set of labels. In some studies, the algorithms are applied to natural...
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ISBN:
(纸本)9781510600546
The multi-instancemulti-label (MIML) learning is a learning framework where each example is described by a bag of instances and corresponding to a set of labels. In some studies, the algorithms are applied to natural scene image classification and have achieved satisfied performance. We design a MIML algorithm based on RBF neural network for the natural scene image classification. In the framework, we compare classification accuracy based on the existing definitions of bag distance: maximum Hausdorff, minimum Hausdorff and average Hausdorff. Although the accuracy of average Hausdorff bag distance is the highest, we find average Hausdorff bag distance to weaken the role of the minimum distance between the instances in the two bags. So we redefine the average Hausdorff bag distance by introducing an adaptive adjustment coefficient, and it can change according to the minimum distance between the instances in the two bags. Finally, the experimental results show that the enhanced algorithm has a better result than the original algorithm.
When looking for a restaurant online, user uploaded photos often give people an immediate and tangible impression about a restaurant. Due to their informativeness, such user contributed photos are leveraged by restaur...
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ISBN:
(纸本)9781467390057
When looking for a restaurant online, user uploaded photos often give people an immediate and tangible impression about a restaurant. Due to their informativeness, such user contributed photos are leveraged by restaurant review websites to provide their users an intuitive and effective search experience. In this paper, we present a novel approach to inferring restaurant types or styles (ambiance, dish styles, suitability for different occasions) from user uploaded photos on user-review websites. To that end, we first collect a novel restaurant photo dataset associating the user contributed photos with the restaurant styles from TripAdvior. We then propose a deep multi-instance multi-label learning (MIML) framework to deal with the unique problem setting of the restaurant style classification task. We employ a two-step bootstrap strategy to train a multi-label convolutional neural network (CNN). The multi-label CNN is then used to compute the confidence scores of restaurant styles for all the images associated with a restaurant. The computed confidence scores are further used to train a final binary classifier for each restaurant style tag. Upon training, the styles of a restaurant can be profiled by analyzing restaurant photos with the trained multi-label CNN and SVM models. Experimental evaluation has demonstrated that our crowd sourcing-based approach can effectively infer the restaurant style when there are a sufficient number of user uploaded photos for a given restaurant.
Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techni...
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Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually predicting functional properties of proteins has become increasingly cumbersome. Such large genomics data sets can only be annotated computationally. However, automated assignment of functions to unknown protein is challenging due to its inherent difficulty and complexity. Previous studies have revealed that solving problems involving complicated objects with multiple semantic meanings using the multi-instancemulti-label (MIML) framework is er effective. For the protein function prediction problems, each protein object in nature may associate with distinct structural units (instances) and multiple functional properties (class labels) where each unit is described by an instance and each functional property is considered as a class label. Thus, it is convenient and natural to tackle the protein function prediction problem by using the MIML framework. In this paper, we propose a sparse Markov chain-based semi-supervised MIML method, called Sparse-Markov. A sparse transductive probability graph is constructed to encode the affinity information of the data based on ensemble of Hausdorff distance metrics. Our goal is to exploit the affinity between protein objects in the sparse transductive probability graph to seek a sparse steady state probability of the Markov chain model to do protein function prediction, such that two proteins are given similar functional labels if they are close to each other in terms of an ensemble Hausdorff distance in the graph. Experimental results on seven real-world organism data sets covering three biological domains show that our proposed Sparse-Markov method is able to achieve better performance than four state-of-the-art MIML learning algorithms.
multi-instance multi-label learning, an extension of multi-instancelearning in multi-label classification, has been successfully used in image classification. In existing algorithms, the distribution of instances in ...
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ISBN:
(纸本)9781467376068
multi-instance multi-label learning, an extension of multi-instancelearning in multi-label classification, has been successfully used in image classification. In existing algorithms, the distribution of instances in bags is generally assumed to be independent of each other, which is difficult to be guaranteed in image classification. Considering instance correlations in a bag, in this paper a novel method of scene classification based on multi-kernel fusion and multi-instance multi-label learning is proposed. First, instance correlations are introduced by means of building graph. Then, different kernel matrices can be derived from kernel functions based on graphs in different scales. Finally, the multi-label can be predicted by the multi-kernel SVM classifier based on multiple-kernel fusion. Experimental results on scene data set and MSRC v2 data set show that the proposed method greatly improves the accuracy of the image scene classification compared with other methods.
With the rapid development of digital cameras, we have witnessed great interest and promise in automatic image annotation as a hot research field. Automatic image annotation is an effective method to resolve the probl...
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With the rapid development of digital cameras, we have witnessed great interest and promise in automatic image annotation as a hot research field. Automatic image annotation is an effective method to resolve the problem of “Semantic Gap”. Automatic Image annotation is a challenging problem when a label is provided for the entire training image only instead of the object region. To eliminate labeling ambiguity, image categorization and object localization should be performed simultaneously. Discriminative multiple instancemulti-labellearning (MIML) can be used for this task by regarding each image as a bag and sub-windows in the image as instances. learning a discriminative multi-instance classifier requires an iterative solution. In each round, positive sub-windows for the next round should be selected. With standard approaches, selecting only one positive sub-window per positive bag may limit the search space for global optimum; meanwhile, selecting all temporal positive sub-windows may add noise into learning. We select a subset of sub-windows per positive bag to avoid those limitations. Our Proposed EMID algorithm is able to take the correlation among instances, correlation among labels, and correlation between instances and labels simultaneously, and provides a very rich representation and learning potential. Experimental results demonstrate that our approach outperforms previous discriminative MIML approaches and standard categorization approaches.
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