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1st international workshop on machine learning, Optimization, and Big data, MOD 2015

ISBN: (纸本)9783319279251

The proceedings contain 32 papers. The special focus in this conference is on machine learning, Optimization, and Big data. The topics include: Automatic tuning of algorithms through sensitivity minimization;step down and step up statistical procedures for stock selection with sharp ratio;differentiating the multipoint expected improvement for optimal batch design;dynamic detection of transportation modes using keypoint prediction;effect of the dynamic topology on the performance of pso-2s algorithm for continuous optimization;heuristic for site-dependent truck and trailer routing problem with soft and hard time windows and split deliveries;cross-domain matrix factorization for multiple implicit-feedback domains;advanced metamodeling techniques applied to multidimensional applications with piecewise responses;alternating direction method of multipliers for regularized multiclass support vector machines;a single-facility manifold location routing problem with an application to supply chain management and robotics;an efficient many-core implementation for semi-supervised support vector machines;intent recognition in a simulated maritime multi-agent domain;an adaptive classification framework for unsupervised model updating in nonstationary environments;global optimization with sparse and local Gaussian process models;condense mixed convexity and optimization with an application in data service optimization;soc-based pattern recognition systems for non destructive testing;an efficient numerical approximation for the Monge-Kantorovich mass transfer problem;outlier detection in COX proportional hazards models based on the concordance c-index and data clustering by particle swarm optimization with the focal particles.

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ASM 2015 - Proceedings of the 1st international workshop on Affect and Sentiment in Multimedia, co-located with ACM MM 2015

ASM 2015 - Proceedings of the 1st International Workshop on ...

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1st international workshop on Affect and Sentiment in Multimedia, ASM 2015

ISBN: (纸本)9781450337502

The proceedings contain 10 papers. The topics discussed include: learning combinations of multiple feature representations for music emotion prediction;twitter: a new online source of automatically tagged data for conversational speech emotion recognition;affect recognition in a realistic movie dataset using a hierarchical approach;do others perceive you as you want them to? modeling personality based on selfies;aesthetic photo enhancement using machine learning and case-based reasoning;prediction of user ratings of oral presentations using label relations;continuous arousal self-assessments validation using real-time physiological responses;an interactive system based on yes-no questions for affective image retrieval;and diving deep into sentiment: understanding fine-tuned CNNs for visual sentiment prediction.

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A deep analysis on age estimation

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pattern recognition LETTERS 2015年第Part2期68卷 239-249页

作者： Huerta, Ivan Fernandez, Carles Segura, Carlos Hernando, Javier Prati, Andrea Univ IUAV DPDCE I-30135 Venice Italy Herta Secur Barcelona 08037 Spain Univ Politecn Cataluna ES-08034 Barcelona Spain

The automatic estimation of age from face images is increasingly gaining attention, as it facilitates applications including advanced video surveillance, demographic statistics collection, customer profiling, or search optimization in large databases. Nevertheless, it becomes challenging to estimate age from uncontrollable environments, with insufficient and incomplete training data, dealing with strong person-specificity and high within-range variance. These difficulties have been recently addressed with complex and strongly hand-crafted descriptors, difficult to replicate and compare. This paper presents two novel approaches: first, a simple yet effective fusion of descriptors based on texture and local appearance;and second, a deep learning scheme for accurate age estimation. These methods have been evaluated under a diversity of settings, and the extensive experiments carried out on two large databases (MORPH and FRGC) demonstrate state-of-the-art results over previous work. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Age estimation Deep learning DNN CCA HOG LBP

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Action2Activity: Recognizing Complex Activities from Sensor data 24

Action2Activity: Recognizing Complex Activities from Sensor ...

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1st international workshop on Social Influence Analysis / 24th international Joint Conference on Artificial Intelligence (IJCAI)

作者： Liu, Ye Nie, Liqiang Han, Lei Zhang, Luming Rosenblum, David S. Natl Univ Singapore Sch Comp Singapore Singapore Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781577357384

As compared to simple actions, activities are much more complex, but semantically consistent with a human's real life. Techniques for action recognition from sensor generated data are mature. However, there has been relatively little work on bridging the gap between actions and activities. To this end, this paper presents a novel approach for complex activity recognition comprising of two components. The first component is temporal pattern mining, which provides a mid-level feature representation for activities, encodes temporal relatedness among actions, and captures the intrinsic properties of activities. The second component is adaptive Multi-Task learning, which captures relatedness among activities and selects discriminant features. Extensive experiments on a real-world dataset demonstrate the effectiveness of our work.

关键词： Artificial intelligence

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The Scaffolded Sound Beehive 24

The Scaffolded Sound Beehive

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1st international workshop on Social Influence Analysis / 24th international Joint Conference on Artificial Intelligence (IJCAI)

作者： Maes, AnneMarie OKNO Brussels Urban Bee Lab Vlaamse Steenweg 66 B-1000 Brussels Belgium

ISBN: (纸本)9781577357384

The Scaffolded Sound Beehive is an immersive multi-media installation which provides viewers an artistic visual and audio experience of activities in a beehive. data were recorded in urban beehives and processed using sophisticated pattern recognition, AI technologies, and sonification and computer graphics software. The installation includes an experiment in using Deep learning to interpret the activities in the hive based on sound and micro-climate recording.

关键词： Deep learning

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Toward shift invariant detection of event-related potentials in non-invasive brain-computer interface

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pattern recognition LETTERS 2015年 66卷 127-134页

作者： Cecotti, Hubert Univ Ulster Sch Comp & Intelligent Syst Coleraine BT52 1SA Londonderry North Ireland

Non-invasive brain-computer interface (BCI) is a relatively new type of human-computer interaction. BCIs that are based on the detection of event-related potentials (ERPs) are usually synchronous. They require the knowledge of the stimulus onsets that evoke ERPs, which is time locked to the presence of a potentially relevant stimulus. The detection of ERPs like the P300 has been successfully used in BCI thanks to the oddball paradigm. The time locked detection is directly related to the synchronous aspect of a BCI. However, asynchronous detection is a critical issue in developing BCIs for real-life applications, where the machine should be able to detect the presence of an ERP independently from the knowledge of the stimulus onsets, or when wireless devices do not allow a precise knowledge of the stimulus onsets. Although the detection of single-trial ERP is already a challenge, when the stimulus onsets are well identified, we propose to investigate further the detection of single-trial ERP by considering different time locked stimuli. We propose and compare shift invariant ERP detection strategies on data from ten subjects obtained in a P300 speller experiment. With a shift invariant distance, we show that it is possible to obtain an AUC of 0.834 while allowing a jitter of +/- 40 ms. With inputs in the Fourier domain, the mean area under the ROC curves of 0.633 allowing a jitter of +/- 200 ms in the stimulus onsets. The results support the conclusion that ERP detection can be achieved without a precise knowledge of the stimulus onsets, and hence can be used with EEG amplifiers that do not allow a precise synchronization between the EEG signal and stimulus onsets. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Event related potentials (ERP) Brain-computer interface Shift invariant detection Feature extraction

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learning Deep Temporal Representations for fMRI Brain Decoding 1st

Learning Deep Temporal Representations for fMRI Brain Decodi...

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1st international workshop on Medical learning Meets Medical Imaging (MLMMI)

作者： Firat, Orhan Aksan, Emre Oztekin, Ilke Vural, Fatos T. Yarman Middle E Tech Univ TR-06531 Ankara Turkey Koc Univ Istanbul Turkey

ISBN: (纸本)9783319279299;9783319279282

Functional magnetic resonance imaging (fMRI) produces low number of samples in high dimensional vector spaces which is hardly adequate for brain decoding tasks. In this study, we propose a combination of autoencoding and temporal convolutional neural network architecture which aims to reduce the feature dimensionality along with improved classification performance. The proposed network learns temporal representations of voxel intensities at each layer of the network by leveraging unlabeled fMRI data with regularized autoencoders. Learned temporal representations capture the temporal regularities of the fMRI data and are observed to be an expressive bank of activation patterns. Then a temporal convolutional neural network with spatial pooling layers reduces the dimensionality of the learned representations. By employing the proposed method, raw input fMRI data is mapped to a low-dimensional feature space where the final classification is conducted. In addition, a simple decorrelated representation approach is proposed for tuning the model hyper-parameters. The proposed method is tested on a ten class recognition memory experiment with nine subjects. Results support the efficiency and potential of the proposed model, compared to the baseline multi-voxel pattern analysis techniques.

关键词： Vector spaces

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Classification of Heart Disease Using Naive Bayes and Genetic Algorithm 1

Classification of Heart Disease Using Naive Bayes and Geneti...

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1st international Conference on Computational Intelligence in data mining (ICCIDM)

作者： Kumar, Santosh Sahoo, G. Birla Inst Technol Dept Comp Sci & Engn Ranchi Jharkhand India

ISBN: (纸本)9788132222088;9788132222071

data mining techniques have been widely used to mine knowledgeable information from medical data bases. In data mining Classification is a supervised learning that can be used to design models describing important data classes, where class attribute is involved in the construction of the classifier. Naive Bayes is very simple, most popular, highly efficient and effective algorithm for pattern recognition. Medical data bases are high volume in nature. If the data set contains redundant and irrelevant attributes, classification may produce less accurate result. Heart disease is the leading cause of death in India as well as different parts of world. Hence there is a need to define a decision support system that helps clinicians to take precautionary measures. In this paper we propose a new algorithm which combines Naive Bayes with genetic algorithm for effective classification. Experimental results shows that our algorithm enhance the accuracy in diagnosis of heart disease.

关键词： Naive Bayes Genetic algorithm Heart disease data mining

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SoC-based pattern recognition systems for Non Destructive Testing 1st

SoC-based pattern recognition systems for Non Destructive Te...

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1st international workshop on machine learning, Optimization, and Big data, MOD 2015

作者： Schiaratura, Omar Ansaloni, Pietro Lughi, Giovanni Neri, Mattia Roffilli, Matteo Serpi, Fabrizio Simonetto, Andrea Bioretics S.r.l. Pula Italy CRS4 S.c.a.r.l. Pula Italy

ISBN: (纸本)9783319279251

Non Destructive Testing (NDT) is one of the most important aspect in modern manufacturing companies. Automation of this task improves productivity and reliability of distribution chains. We present an optimized implementation of common pattern recognition algorithms that performs NDT on factory products. To the aim of enhancing the industrial integration, our implementation is highly optimized to work on SoC-based (System on Chip: an integrated circuit that integrates all components of a computer into a single chip.) hardware and we worked with the initial idea of an overall design for these devices. While perfectly working on general purpose SoCs, the best performances are achieved on GPU accelerated ones. We reached the notable performance of a PC-based workstation by exploiting technologies like CUDA and BLAS for embedded SoCs. The test case is a collection of toy scenarios commonly found in manufacturing companies. © Springer international Publishing Switzerland 2015.

关键词： Nondestructive examination

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Feature Selection for Multi-Label learning 24

Feature Selection for Multi-Label Learning

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1st international workshop on Social Influence Analysis / 24th international Joint Conference on Artificial Intelligence (IJCAI)

作者： Spolaor, Newton Monard, Maria Carolina Lee, Huei Diana Univ Sao Paulo Inst Math & Comp Sci Lab Computat Intelligence Sao Carlos Brazil State Univ West Parana Lab Bioinformat Foz Do Iguacu Brazil

ISBN: (纸本)9781577357384

Feature Selection plays an important role in machine learning and data mining, and it is often applied as a data pre-processing step. This task can speed up learning algorithms and sometimes improve their performance. In multi-label learning, label dependence is considered another aspect that can contribute to improve learning performance. A replicable and wide systematic review performed by us corroborates this idea. Based on this information, it is believed that considering label dependence during feature selection can lead to better learning performance. The hypothesis of this work is that multi-label feature selection algorithms that consider label dependence will perform better than the ones that disregard it. To this end, we propose multi-label feature selection algorithms that take into account label relations. These algorithms were experimentally compared to the standard approach for feature selection, showing good performance in terms of feature reduction and predictability of the classifiers built using the selected features.

关键词： Feature Selection

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