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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Chen, Si-Bao Wang, Hai-Xian Luo, Bin Key Lab. of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei Anhui 230039 China Key Lab. of Child Development and Learning Science Research Center for Learning Science Southeast University Nanjing Jiangsu 210096 China

ISBN: (纸本)9780769541099

Although many methods of refining initialization have appeared, the sensitivity of K-Means to initial centers is still an obstacle in applications. In this paper, we investigate a new class of clustering algorithm, K-Alpha Means (KAM), which is insensitive to the initial centers. With K-Harmonic Means as a special case, KAM dynamically weights data points during iteratively updating centers, which deemphasizes data points that are close to centers while emphasizes data points that are not close to any centers. Through replacing minimum operator in K-Means by alpha-mean operator, KAM significantly improves the clustering performances. © 2010 IEEE.

关键词： Clustering algorithms

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An efficient minimum vocabulary construction algorithm for language modeling 12

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25th International Conference on Industrial Engineering and Other Applications of Applied intelligent Systems, IEA/AIE 2012

作者： Lin, Sina Qin, Zengchang Huang, Zehua Wan, Tao Intelligent Computing and Machine Learning Lab. School of ASEE Beihang University Beijing China School of Advanced Engineering Beihang University China Robotics Institute Carnegie Mellon University Pittsburgh United States School of Medicine Boston University Boston United States

ISBN: (纸本)9783642310867

In learning a new word by a dictionary, we first need to know a set of "basic words" which are frequently appeared in word definitions. It often happens that you cannot understand the word you looked up because there are still some words you do not understand in its definitions or explanations provided by the dictionary. You can keep looking up these new words recursively till they all can be well explained by some basic words you already knew. How to automatically find a minimum set of such basic words to define (or recursively define) the entire vocabulary in a given dictionary is what are going to discuss in this paper. We propose an efficient algorithm to construct the Minimum Vocabulary (MV) using the word frequency information. The minimum vocabulary can be used for language modeling and experimental results demonstrate the effectiveness of using the minimum vocabulary as features in text classification. © 2012 Springer-Verlag.

关键词： Classification (of information)

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intelligent Model for Predicting the Quality of Services Violation

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Computers, Materials & Continua 2022年第5期71卷 3607-3619页

作者： Muhammad Adnan Khan Asma Kanwal Sagheer Abbas Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab. Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Cloud computing is providing IT services to its customer based on Service level agreements(SLAs).It is important for cloud service providers to provide reliable Quality of service(QoS)and to maintain SLAs *** service providers need to predict possible service violations before the emergence of an issue to perform remedial actions for *** users’major concerns;the factors for service reliability are based on response time,accessibility,availab.lity,and *** this paper,we,therefore,experiment with the parallel mutant-Particle swarm optimization(PSO)for the detection and predictions of QoS violations in terms of response time,speed,accessibility,and *** paper also compares Simple-PSO and Parallel *** simulation results,it is observed that the proposed Parallel MutantPSO solution for cloud QoS violation prediction achieves 94%accuracy which is many accurate results and is computationally the fastest technique in comparison of conventional PSO technique.

关键词： Accountability particle swarm optimization mutant particle swarm optimization quality of service service level agreement

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WAVELET-BASED STATISTICAL FEATURES FOR DISTINGUISHING MITOTIC AND NON-MITOTIC CELLS IN BREAST CANCER HISTOPATHOLOGY

WAVELET-BASED STATISTICAL FEATURES FOR DISTINGUISHING MITOTI...

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IEEE International Conference on Image Processing

作者： Tao Wan Xu Liu Jianhui Chen Zengchang Qin Intelligent Computing and Machine Learning Lab Beihang University No 91 Central Hospital of PLA

ISBN: (纸本)9781479957521

To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopatho-logical images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis classification by characterizing objects of interest (tissue cells) in wavelet based multi-resolution representations that better capture the statistical features having mitosis discrimination. A dual-tree complex wavelet transform (DT-CWT) is performed to decompose the image patches into multi-scale forms. Five commonly-used statistical features are extracted on each wavelet subband. Since both mitotic and non-mitotic cells appear as small objects with a large variety of shapes in the images, characterization of mitosis is a challenging problem. The inter-scale dependencies of wavelet coefficients allow extraction of important texture features within the cells that are more likely to appear at all different scales. The wavelet-based statistical features were evaluated on a dataset containing 327 mitotic and 406 non-mitotic cells via a support vector machine classifier in iterative cross-validation. The quantitative results showed that our DT-CWT based approach achieved superior classification performance with the accuracy of 87.94%, sensitivity of 86.80%, specificity of 89.89%, and the area under the curve (AUC) value of 0.94.

关键词： Mitosis Wavelet transform Multi-resolution representation Breast cancer histopathology

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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7th IFIP International Conference on intelligent Information Processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalab.lity of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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Real-time head detection with kinect for driving fatigue detection

Real-time head detection with kinect for driving fatigue det...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Cao, Yang Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642420504

Nowadays, depth cameras such Microsoft Kinect make it easier and cheaper for us to capture depth images. It becomes practical to use depth images for detection in consumer-grade products. In this paper, we propose a novel and simple real-time method to detect human head in depth image for our driving fatigue detection system, based on the elliptical shape of human head. Experiments show that our method can successfully detect human head in different light conditions and across different head poses. We integrate this detection algorithm into our driving fatigue detection system, and see remarkable improvements both in detection rate and detection speed. © Springer-Verlag 2013.

关键词： Computers

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Wavelet-based statistical features for distinguishing mitotic and non-mitotic cells in breast cancer histopathology

Wavelet-based statistical features for distinguishing mitoti...

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作者： Wan, Tao Liu, Xu Chen, Jianhui Qin, Zengchang Intelligent Computing and Machine Learning Lab Beihang University Beijing100191 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China No 91 Central Hospital of PLA Henan454003 China

ISBN: (纸本)9781479957514

To diagnose breast cancer (BCa), the number of mitotic cells present in tissue sections is an important parameter to examine and grade breast biopsy specimen. The differentiation of mitotic from non-mitotic cells in breast histopathological images is a crucial step for automatical mitosis detection. This work aims at improving the accuracy of mitosis classification by characterizing objects of interest (tissue cells) in wavelet based multi-resolution representations that better capture the statistical features having mitosis discrimination. A dual-tree complex wavelet transform (DT-CWT) is performed to decompose the image patches into multi-scale forms. Five commonly-used statistical features are extracted on each wavelet subband. Since both mitotic and non-mitotic cells appear as small objects with a large variety of shapes in the images, characterization of mitosis is a challenging problem. The inter-scale dependencies of wavelet coefficients allow extraction of important texture features within the cells that are more likely to appear at all different scales. The wavelet-based statistical features were evaluated on a dataset containing 327 mitotic and 406 non-mitotic cells via a support vector machine classifier in iterative cross-validation. The quantitative results showed that our DT-CWT based approach achieved superior classification performance with the accuracy of 87.94%, sensitivity of 86.80%, specificity of 89.89%, and the area under the curve (AUC) value of 0.94. © 2014 IEEE.

关键词： Wavelet transforms

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Can Gaussian Sketching Converge Faster on a Preconditioned Landscape? 41

Can Gaussian Sketching Converge Faster on a Preconditioned L...

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41st International Conference on machine learning, ICML 2024

作者： Wang, Yilong Ye, Haishan Dai, Guang Tsang, Ivor W. Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China SGIT AI Lab State Grid Corporation of China China Singapore College of Computing and Data Science NTU Singapore

This paper focuses on the large-scale optimization which is very popular in the big data era. The gradient sketching is an important technique in the large-scale optimization. Specifically, the random coordinate descent algorithm is a kind of gradient sketching method with the random sampling matrix as the sketching matrix. In this paper, we propose a novel gradient sketching called GSGD (Gaussian Sketched Gradient Descent). Compared with the classical gradient sketching methods such as the random coordinate descent and SEGA (Hanzely et al., 2018), our GSGD does not require the importance sampling but can achieve a fast convergence rate matching the ones of these methods with importance sampling. Furthermore, if the objective function has a non-smooth regularization term, our GSGD can also exploit the implicit structure information of the regularization term to achieve a fast convergence rate. Finally, our experimental results substantiate the effectiveness and efficiency of our algorithm. Copyright 2024 by the author(s)

关键词： Big data

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learning efficient constraint graph sampling for robotic sequential manipulation

arXiv

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arXiv 2020年

作者： Ortiz-Haro, Joaquim Hartmann, Valentin N. Oguz, Ozgur S. Toussaint, Marc Machine Learning & Robotics Lab. University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Learning and Intelligent Systems Lab. TU Berlin Germany

Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the underlying nonlinear program is decomposed into differentiable equality and inequality constraints, each of which depends only on some variables. Such problems are at the core of efficient and robust sequential robot manipulation planning. Naive sequential conditional sampling of individual variables, as well as fully joint sampling of all variables at once (e.g., leveraging optimization methods), can be highly inefficient and non-robust. We propose a novel framework to learn how to break the overall problem into smaller sequential sampling problems. Specifically, we leverage Monte-Carlo Tree Search to learn assignment orders for the variable-subsets, in order to minimize the computation time to generate feasible full samples. This strategy allows us to efficiently compute a set of diverse valid robot configurations for mode-switches within sequential manipulation tasks, which are waypoints for subsequent trajectory optimization or sampling-based motion planning algorithms. We show that the learning method quickly converges to the best sampling strategy for a given problem, and outperforms user-defined orderings or fully joint optimization, while providing a higher sample diversity. © 2020, CC BY.

关键词： Constraint theory

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Double Stochasticity Gazes Faster: Snap-Shot Decentralized Stochastic Gradient Tracking Methods 41

Double Stochasticity Gazes Faster: Snap-Shot Decentralized S...

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41st International Conference on machine learning, ICML 2024

作者： Di, Hao Ye, Haishan Chang, Xiangyu Dai, Guang Tsang, Ivor W. Center for Intelligent Decision-Making and Machine Learning School of Management Xi'an Jiaotong University China SGIT AI Lab State Grid Corporation of China China College of Computing and Data Science NTU Singapore Singapore

In decentralized optimization, m agents form a network and only communicate with their neighbors, which gives advantages in data ownership, privacy, and scalab.lity. At the same time, decentralized stochastic gradient descent (SGD) methods, as popular decentralized algorithms for training large-scale machine learning models, have shown their superiority over centralized counterparts. Distributed stochastic gradient tracking (DSGT) (Pu & Nedić, 2021) has been recognized as the popular and state-of-the-art decentralized SGD method due to its proper theoretical guarantees. However, the theoretical analysis of DSGT (Koloskova et al., 2021) shows that its iteration complexity is (equation presented) where the doubly stochastic matrix W represents the network topology and CW is a parameter that depends on W. Thus, it indicates that the convergence property of DSGT is heavily affected by the topology of the communication network. To overcome the weakness of DSGT, we resort to the snapshot gradient tracking skill and propose two novel algorithms, snap-shot DSGT (SS DSGT) and accelerated snap-shot DSGT (ASS DSGT). We further justify that SS DSGT exhibits a lower iteration complexity compared to DSGT in the general communication network topology. Additionally, ASS DSGT matches DSGT's iteration complexity (equation presented) under the same conditions as DSGT. Numerical experiments validate SS DSGT's superior performance in the general communication network topology and exhibit better practical performance of ASS DSGT on the specified W compared to DSGT. Copyright 2024 by the author(s)

关键词： Network topology

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