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Novel analog circuit fault diagnosis method based on SVM of learning using privileged information
基于融合特权信息支持向量机的模拟电路故障诊断新方法

LI Tao-zhu,LI Hong-bo,ZENG Fan-jing,LI Tie-feng,
李涛柱
,李红波,曾繁景,李铁峰

计算机应用研究 , 2012,
Abstract: This paper proposed a novel fault diagnosis method based on SVM of learning using privileged information (LUPI-SVM),aiming at solving the problem of correctly identifying fault classes in analog circuit fault diagnosis.Firstly, the fault feature vectors were extracted by PCA(principal component analysis) feature extraction method. Then, after training the LUPI-SVM by faulty feature vectors, the LUPI-SVM model of the circuit fault diagnosis system was built. Finally, input the test samples' feature vectors into the trained LUPI-SVM model to identify the different fault cases. The simulation results for analog and mixed-signal test benchmark Sallen-Key filter circuits demonstrate that the proposed method improves classification ability. It correctly classifies not only the single hard fault classes with a highly average classification success rate more than 99%, but also the multiple fault classes.The method develops a new direction for the fault diagnosis of analog circuit.
Learning to Transfer Privileged Information  [PDF]
Viktoriia Sharmanska,Novi Quadrianto,Christoph H. Lampert
Computer Science , 2014,
Abstract: We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching computers to recognize objects in images. We want the computers to be able to learn faster at the expense of providing extra information during training time. As additional information about the image data, we look at several scenarios that have been studied in computer vision before: attributes, bounding boxes and image tags. The information is privileged as it is available at training time but not at test time. We explore two maximum-margin techniques that are able to make use of this additional source of information, for binary and multiclass object classification. We interpret these methods as learning easiness and hardness of the objects in the privileged space and then transferring this knowledge to train a better classifier in the original space. We provide a thorough analysis and comparison of information transfer from privileged to the original data spaces for both LUPI methods. Our experiments show that incorporating privileged information can improve the classification accuracy. Finally, we conduct user studies to understand which samples are easy and which are hard for human learning, and explore how this information is related to easy and hard samples when learning a classifier.
Incremental SVM Intrusion Detection Algorithm Based on Distance Weighted Template Reduction and Attribute Information Entropyc
基于距离加权模板约简和属性信息嫡的增量SVM入侵检测算法

徐永华,李广水
计算机科学 , 2012,
Abstract: In order to solve the problem of the SVM intrusion detection method which has low detection rate, high disforting rate and slow detection speed, a kind of incremental SVM intrusion detection algorithm based on distance weighfed template reduction and the attribute information entropy was proposed. In this algorithm, the training sample set reduction is made according to the sample for the samples and the neighbors to the total distance weighted weight, then,the clustering sample point and the noise of the fitting point are taken out through the adjacent to the border area segmentation and based on sample attribute information entropy, and then, using the sample dispersion extracts possible support vector machine, and incremental learning based on KK I} conditions is made to construct the optimal SVM classifier. The simulation results show that the algorithm has good detection rate and the detection efficiency, and distorting rate low.
Mind the Nuisance: Gaussian Process Classification using Privileged Noise  [PDF]
Daniel Hernández-Lobato,Viktoriia Sharmanska,Kristian Kersting,Christoph H. Lampert,Novi Quadrianto
Computer Science , 2014,
Abstract: The learning with privileged information setting has recently attracted a lot of attention within the machine learning community, as it allows the integration of additional knowledge into the training process of a classifier, even when this comes in the form of a data modality that is not available at test time. Here, we show that privileged information can naturally be treated as noise in the latent function of a Gaussian Process classifier (GPC). That is, in contrast to the standard GPC setting, the latent function is not just a nuisance but a feature: it becomes a natural measure of confidence about the training data by modulating the slope of the GPC sigmoid likelihood function. Extensive experiments on public datasets show that the proposed GPC method using privileged noise, called GPC+, improves over a standard GPC without privileged knowledge, and also over the current state-of-the-art SVM-based method, SVM+. Moreover, we show that advanced neural networks and deep learning methods can be compressed as privileged information.
Privileged Information for Data Clustering  [PDF]
Jan Feyereisl,Uwe Aickelin
Computer Science , 2013,
Abstract: Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case of unsupervised learning, or the input and output space X x Y in the case of supervised and semi-supervised learning. In the last number of years the relaxation of this assumption has been explored and the importance of incorporation of additional information within machine learning algorithms became more apparent. Traditionally such fusion of information was the domain of semi-supervised learning. More recently the inclusion of knowledge from separate hypothetical spaces has been proposed by Vapnik as part of the supervised setting. In this work we are interested in exploring Vapnik's idea of master-class learning and the associated learning using privileged information, however within the unsupervised setting. Adoption of the advanced supervised learning paradigm for the unsupervised setting instigates investigation into the difference between privileged and technical data. By means of our proposed aRi-MAX method stability of the KMeans algorithm is improved and identification of the best clustering solution is achieved on an artificial dataset. Subsequently an information theoretic dot product based algorithm called P-Dot is proposed. This method has the ability to utilize a wide variety of clustering techniques, individually or in combination, while fusing privileged and technical data for improved clustering. Application of the P-Dot method to the task of digit recognition confirms our findings in a real-world scenario.
Unifying distillation and privileged information  [PDF]
David Lopez-Paz,Léon Bottou,Bernhard Sch?lkopf,Vladimir Vapnik
Computer Science , 2015,
Abstract: Distillation (Hinton et al., 2015) and privileged information (Vapnik & Izmailov, 2015) are two techniques that enable machines to learn from other machines. This paper unifies these two techniques into generalized distillation, a framework to learn from multiple machines and data representations. We provide theoretical and causal insight about the inner workings of generalized distillation, extend it to unsupervised, semisupervised and multitask learning scenarios, and illustrate its efficacy on a variety of numerical simulations on both synthetic and real-world data.
Combining Privileged Information to Improve Context-Aware Recommender Systems  [PDF]
Camila V. Sundermann,Marcos A. Domingues,Ricardo M. Marcacini,Solange O. Rezende
Computer Science , 2015,
Abstract: A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems. The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II). We evaluated the contextual information in four context-aware recommender systems. Different weights were assigned to each type of information. The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.
Incremental Learning with SVM for Multimodal Classification of Prostatic Adenocarcinoma  [PDF]
José Fernando García Molina, Lei Zheng, Metin Sertdemir, Dietmar J. Dinter, Stefan Sch?nberg, Matthias R?dle
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0093600
Abstract: Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844±0.068 and a specificity of 0.780±0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.
$\propto$SVM for learning with label proportions  [PDF]
Felix X. Yu,Dong Liu,Sanjiv Kumar,Tony Jebara,Shih-Fu Chang
Computer Science , 2013,
Abstract: We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. We propose a new method called proportion-SVM, or $\propto$SVM, which explicitly models the latent unknown instance labels together with the known group label proportions in a large-margin framework. Unlike the existing works, our approach avoids making restrictive assumptions about the data. The $\propto$SVM model leads to a non-convex integer programming problem. In order to solve it efficiently, we propose two algorithms: one based on simple alternating optimization and the other based on a convex relaxation. Extensive experiments on standard datasets show that $\propto$SVM outperforms the state-of-the-art, especially for larger group sizes.
Web Information Clustering by Personal Search Engine Based on SVM
Wang deji,Li mincheng,Xiong fanlun
Asian Journal of Information Technology , 2012,
Abstract: Web information is scaling more than exponentially with time. How to acquire information efficiently by personal search engine is staring us in our faces. Personal preference can not be easily described but can be learned quickly from the examples. Although PCC (pairwise classification clustering) is a powerful tool for learning the examples, but transitive dependences dwarf it. In this paper, we introduce clustering with SVM and define semantic cosine similarity based ontology to solve this problem. Experiments proof that it is efficient and powerful.
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