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Investigation on the construction of the Relevance Vector Machine based on cross entropy minimization

Investigation on the construction of the Relevance Vector Ma...

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international conference on Automation and Computing (ICAC)

作者： Xiaofang Liu Ruikang Li Dansong Cheng Kai Cheng School of Electrical Engineering & Automation Harbin Institute of Technology Harbin China Shanghai Institute of Mechanical & Electrical Engineering Shanghai China Institute of Materials & Manufacturing Brunel University London Uxbridge UK

As a machine learning method under sparse Bayesian framework, classical Relevance Vector Machine (RVM) applies kernel methods to construct Radial Basis Function(RBF) networks using a least number of relevant basis functions. Compared to the well-known Support Vector Machine (SVM), the RVM provides a better sparsity, and an automatic estimation of hyperparameters. However, the performance of the original RVM purely depends on the smoothness of the presumed prior of the connection weights and parameters. Consequently, the sparsity is actually still controlled by the selection of kernel functions or kernel parameters. This may lead to severe underfitting or overfitting in some cases. In the research presented in this paper, we explicitly involve the number of basis functions into the objective of the optimization procedure, and construct the RVM by the minimization of the cross entropy between the “hypothetical” probability distribution in the forward training pathway and the “true” probability distribution in the backward testing pathway. The experimental results have shown that our proposed methodology can achieve both the least complexity of structure and goodness of appropriate fit to data.

关键词： Training Mathematical model Entropy Kernel Support vector machines Minimization Bayes methods

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Timed dataflow: Reducing Communication Overhead for Distributed Machine learning Systems

Timed Dataflow: Reducing Communication Overhead for Distribu...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Peng Sun Yonggang Wen Ta Nguyen Binh Duong Shengen Yan School of Computer Science and Engineering Nanyang Technological University Singapore Sch. of Comput. Sci. & Eng. Nanyang Technological University Singapore Singapore SG Sensetime Group Limited Shatin N.T Hong Kong

ISBN: (纸本)9781509053827

Many distributed machine learning (ML) systems exhibit high communication overhead when dealing with big data sets. Our investigations showed that popular distributed ML systems could spend about an order of magnitude more time on network communication than computation to train ML models containing millions of parameters. Such high communication overhead is mainly caused by two operations: pulling parameters and pushing gradients. In this paper, we propose an approach called Timed dataflow (TDF) to deal with this problem via reducing network traffic using three techniques: a timed parameter storage system, a hybrid parameter filter and a hybrid gradient filter. In particular, the timed parameter storage technique and the hybrid parameter filter enable servers to discard unchanged parameters during the pull operation, and the hybrid gradient filter allows servers to drop gradients selectively during the push operation. Therefore, TDF could reduce the network traffic and communication time significantly. Extensive performance evaluations in a real testbed showed that TDF could reduce up to 77% and 79% of network traffic for the pull and push operations, respectively. As a result, TDF could speed up model training by a factor of up to 4 without sacrificing much accuracy for some popular ML models, compared to systems not using TDF.

关键词： Servers Training data Training Synchronization Bandwidth Optimization Open area test sites

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PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection

PFrauDetector: A Parallelized Graph Mining Approach for Effi...

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international conference on Parallel and Distributed Systems (ICPADS)

作者： Josh Jia-Ching Ying Ji Zhang Che-Wei Huang Kuan-Ta Chen Vincent S. Tseng Department of Computer Science and Information Engineering Feng Chia University Taiwan ROC Faculty of Health University of Southern Queensland Australia Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Department of Computer Science and Information Engineering National Cheng Kung University Taiwan ROC Institute of Information Science Academia Sinica Taiwan ROC Department of Computer Science National Chiao Tung University Taiwan ROC

ISBN: (纸本)9781509053827

In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly-efficient parallelized graph-mining-based fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a "fraud" tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable sub-networks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods.

关键词： Communications technology Training Real-time systems Feature extraction Computer science Training data Parallel processing

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An Autonomous Mobile Robot with Functions of Action learning, Memorizing, Recall and Identifying the Environment Using Gaussian Mixture Model 22nd

An Autonomous Mobile Robot with Functions of Action Learning...

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22nd international conference on Neural Information Processing (ICONIP)

作者： Obayashi, Masanao Yamane, Taiki Kuremoto, Takashi Mabu, Shingo Kobayashi, Kunikazu Yamaguchi Univ 2-16-1 Tokiwadai Ube Yamaguchi 7558611 Japan Aichi Prefectural Univ Nagakute Aichi 4801198 Japan

ISBN: (纸本)9783319265322;9783319265315

In this paper, behavior scheme of autonomous mobile robots to achieve the objectives of them in environments are proposed, having function of identifying the current environment in which they are placed and making use of learning, memorizing and recalling behaviors of corresponding to each of plural different environments. Specifically, each robot has the function of identifying the environment using some behavioral statistical data for each environment, and if the robot has already experienced the environment, it behaves by making use of own experienced data stored in the database, otherwise it performs a new behavior learning and adds the learning results into the database.

关键词： intelligent robot Chaotic neural network Reinforcement learning Identification of the environment Gaussian mixture model

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Minimal Hough Forest training for pattern detection 22

Minimal Hough Forest training for pattern detection

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22nd international conference on Systems, Signals and Image Processing (IWSSIP)

作者： Henderson, Craig Izquierdo, Ebroul Queen Mary Univ London London England

ISBN: (纸本)9781467383530

This paper assesses Hough Forest configuration parameters with respect to their impact on runtime performance and precision of pattern detection, without large-scale training. The Hough Forest is trained using a very small training set of data and parameters are tuned in a number of experiments, assessing the impact on pattern detection accuracy. A novel method to improve training performance and precision by adaptive selection of the patch size and calculating the used number of patches is introduced. Results are presented using challenging street-scene videos, and demonstrate that the proposed method improves precision and performance over general-purpose Hough Forest parameters used in the literature.

关键词： Hough Forest Pattern Detection Machine learning

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Compressive sensing for power spectrum estimation of multi-dimensional processes under missing data 22

Compressive sensing for power spectrum estimation of multi-d...

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22nd international conference on Systems, Signals and Image Processing, IWSSIP 2015

作者： Zhang, Yuanjin Comerford, Liam Beer, Michael Kougioumtzoglou, Ioannis Institute for Risk and Uncertainty University of Liverpool L69 3GH United Kingdom Department of Civil Engineering and Engineering Mechanics Columbia University New YorkNY10027 United States

ISBN: (纸本)9781467383530

A compressive sensing (CS) based approach is applied in conjunction with an adaptive basis re-weighting procedure for multi-dimensional stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. By relying on signal sparsity, and the assumption that multiple records are available upon which to produce a spectral estimate, it has been shown that a re-weighted CS approach succeeds in estimating power spectra with satisfactory accuracy. Of key importance in this paper is the extension from one-dimensional vector processes to a broader class of problems involving multidimensional stochastic fields. Numerical examples demonstrate the effectiveness of the approach when records are subjected to up to 75% missing data. © 2015 IEEE.

关键词： Power spectrum

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An Auto-Encoder for learning Conversation Representation Using LSTM 22nd

An Auto-Encoder for Learning Conversation Representation Usi...

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22nd international conference on Neural Information Processing (ICONIP)

作者： Zhou, Xiaoqiang Hu, Baotian Chen, Qingcai Wang, Xiaolong Harbin Inst Technol Shenzhen Grad Sch Intelligent Comp Res Ctr Shenzhen Peoples R China

ISBN: (纸本)9783319265322;9783319265315

In this paper, an auto-encoder is proposed to learn conversation representation. First, the long short term memory (LSTM) neural network is used to encode the sequence of sentences in a conversation. The interactive context is encoded into a fixed-length vector. Then, through the LSTM-decoder, the learnt representation is used to reconstruct the sentence vectors of a conversation. To train our model, we construct one corpus with 32,881 conversations from the online shopping platform. Finally, experiments on topic recognition task demonstrate the effectiveness of the proposed auto-encoder on learning conversation representation, especially when training data of topic recognition is relatively small.

关键词： Auto-encoder LSTM Conversation representation

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Hybrid Model for the Training of Interval Type-2 Fuzzy Logic System 22nd

Hybrid Model for the Training of Interval Type-2 Fuzzy Logic...

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22nd international conference on Neural Information Processing (ICONIP)

作者： Hassan, Saima Khosravi, Abbas Jaafar, Jafreezal Khanesar, Mojtaba Ahmadieh Univ Teknol PETRONAS Dept CIS Tronoh Malaysia Kohat Univ Sci & Technol Kohat Pakistan Deakin Univ Ctr Intelligent Syst Res Dandenong Australia Semnan Univ Fac Elect & Comp Engn Semnan Iran

ISBN: (纸本)9783319265322;9783319265315

In this paper, a hybrid training model for interval type-2 fuzzy logic system is proposed. The hybrid training model uses extreme learning machine to tune the consequent part parameters and genetic algorithm to optimize the antecedent part parameters. The proposed hybrid learning model of interval type-2 fuzzy logic system is tested on the prediction of Mackey-Glass time series data sets with different levels of noise. The results are compared with the existing models in literature;extreme learning machine and Kalman filter based learning of consequent part parameters with randomly generated antecedent part parameters. It is observed that the interval type-2 fuzzy logic system provides improved performance with the proposed hybrid learning model.

关键词： Hybrid learning model Extreme learning machine Genetic algorithm Interval type-2 fuzzy logic system Prediction

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learning A Joint Discriminative-Generative Model for Action Recognition 22

Learning A Joint Discriminative-Generative Model for Action ...

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22nd international conference on Systems, Signals and Image Processing (IWSSIP)

作者： Alexiou, Ioannis Xiang, Tao Gong, Shaogang Queen Mary Univ London Sch Elect Engn & Comp Sci London England Vis Semant Ltd London England

ISBN: (纸本)9781467383530

An action consists of a sequence of instantaneous motion patterns whose temporal ordering contains critical information especially for distinguishing fine-grained action categories. However, existing action recognition methods are dominated by discriminative classifiers such as kernel machines or metric learning with Bag-of-Words (BoW) action representations. They ignore the temporal structures of actions in exchange for robustness against noise. Although such temporal structures can be modelled explicitly using dynamic generative models such as Hidden Markov Models (HMMs), these generative models are designed to maximise the likelihood of the data therefore providing no guarantee on suitability for discrimination required by action recognition. In this work, a novel approach is proposed to explore the best of both worlds by discriminatively learning a generative action model. Specifically, our approach is based on discriminative Fisher kernel learning which learns a dynamic generative model so that the distance between the log-likelihood gradients induced by two actions of the same class is minimised. We demonstrate the advantages of the proposed model over the state-of-the-art action recognition methods using two challenging benchmark datasets of complex actions.

关键词： Joint learning Discriminative-Generative Models HMM FKL

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A scalable boosting learner using adaptive sampling 22nd

A scalable boosting learner using adaptive sampling

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22nd international Symposium on Methodologies for intelligent Systems, ISMIS 2015

作者： Chen, Jianhua Burleigh, Seth Chennupati, Neeharika Gudapati, Bharath K. Division of Computer Science and Engineering School of Electrical Engineering and Computer Science Louisiana State University Baton RougeLA70803-4020 United States Department of Physics and Astronomy Louisiana State University Baton RougeLA70803-4020 United States

ISBN: (纸本)9783319252513

Sampling is an important technique for parameter estimation and hypothesis testing widely used in statistical analysis, machine learning and knowledge discovery. Sampling is particularly useful in data mining when the training data set is huge. In this paper, we present a new sampling-based method for learning by Boosting. We show how to utilize the adaptive sampling method in [2] for estimating classifier accuracy in building an efficient ensemble learning method by Boosting. We provide a preliminary theoretical analysis of the proposed sampling-based boosting method. Empirical studies with 4 datasets from UC Irvine ML database show that our method typically uses much smaller sample size (and is thus much more efficient) while maintaining competitive prediction accuracy compared with Watanabe’s sampling-based Boosting learner Madaboost. © Springer international Publishing Switzerland 2015.

关键词： Adaptive boosting

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