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检索条件"任意字段=8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012"

共 106 条记录，以下是1-10 订阅

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Deep Reinforcement learning for Exact Combinatorial Optimization: learning to Branch 26

Deep Reinforcement Learning for Exact Combinatorial Optimiza...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zhang, Tianyu Banitalebi-Dehkordi, Amin Zhang, Yong Univ Alberta Edmonton AB Canada Huawei Technol Canada Co Ltd Vancouver BC Canada

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Branch-and-bound is a systematic enumerative method for combinatorial optimization, where the performance highly relies on the variable selection strategy. State-of-theart handcrafted heuristic strategies suffer from relatively slow inference time for each selection, while the current machine learning methods require a significant amount of labeled data. We propose a new approach for solving the data labeling and inference latency issues in combinatorial optimization based on the use of the reinforcement learning (RL) paradigm. We use imitation learning to bootstrap an RL agent and then use Proximal Policy Optimization (PPO) to further explore global optimal actions. then, a value network is used to run Monte-Carlo tree search (MCTS) to enhance the policy network. We evaluate the performance of our method on four different categories of combinatorial optimization problems and show that our approach performs strongly compared to the state-of-the-art machine learning and heuristics based methods.

关键词： Deep learning Systematics Monte Carlo methods NP-hard problem Input variables Reinforcement learning pattern recognition

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Dynamic data Augmentation with Gating Networks for Time Series recognition 26

Dynamic Data Augmentation with Gating Networks for Time Seri...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Oba, Daisuke Matsuo, Shinnosuke Iwana, Brian Kenji Kyushu Univ Dept Adv Informat Technol Fukuoka Japan

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation is a technique to improve the generalization ability of machine learning methods by increasing the size of the dataset. However, since every augmentation method is not equally effective for every dataset, you need to select an appropriate method carefully. We propose a neural network that dynamically selects the best combination of data augmentation methods using a Gating Network and a mutually beneficial feature consistency loss. the Gating Network is able to control how much of each data augmentation is used for the representation within the network. the feature consistency loss gives a constraint that augmented features from the same input pattern should be in similar. In the experiments, we demonstrate the effectiveness of the proposed method on the 12 largest time-series datasets from 2018 UCR Time Series Archive and reveal the relationships between the data augmentation methods through analysis of the proposed method.

关键词： Histograms Analytical models Time series analysis Neural networks Radar machine learning pattern recognition

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Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption 26

Discovery of New Multi-Level Features for Domain Generalizat...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Frikha, Ahmed Krompass, Denis Tresp, Volker Siemens Technol Mumbai Maharashtra India Univ Munich LMU Munich Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain generalization (DG) problem, where a model trained on a set of source domains is expected to generalize well in unseen domains without any exposure to their data. the main challenge of DG is that the features learned from the source domains are not necessarily present in the unseen target domains, leading to performance deterioration. We assume that learning a richer set of features is crucial to improve the transfer to a wider set of unknown domains. For this reason, we propose COLUMBUS, a method that enforces new feature discovery via a targeted corruption of the most relevant input and multi-level representations of the data. We conduct an extensive empirical evaluation to demonstrate the effectiveness of the proposed approach which achieves new state-of-the-art results by outperforming 18 DG algorithms on multiple DG benchmark datasets in the DOMAINBED framework.

关键词： Training machine learning Benchmark testing data models pattern recognition

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Online Adaptive Metrics for Model Evaluation on Non-representative Offline Test data 26

Online Adaptive Metrics for Model Evaluation on Non-represen...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Piovano, Enrico Le, Dieu-thu Chen, Bei Bradford, Melanie Amazon Com Inc Bellevue WA 98004 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A major challenge encountered in the offline evaluation of machine learning models before being released to production is the discrepancy between the distributions of the offline test data and of the online data, due to, e.g., biased sampling scheme, data aging issues and occurrence(s) of regime shift. Consequently, the offline evaluation metrics often do not reflect the actual performance of the model online. In this paper, we propose online adaptive metrics, a computationally efficient method which re-weights the offline metrics based on calculating the joint distributions of the model hypotheses over the offline test data VS. the online data. It provides offline metrics which estimate the production performance of the model by taking into account the test data biases. the proposed method is demonstrated by real life examples on text classification and a commercial natural language understanding system. We show that the online adaptive metrics can provide accurate predictions of online recall and precision even with a small test dataset.

关键词： Measurement Adaptation models Computational modeling Text categorization Production machine learning data models

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Solar Flare Forecasting with Deep learning-based Time Series Classifiers 26

Solar Flare Forecasting with Deep Learning-based Time Series...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Ji, Anli Wen, Junzhi Angryk, Rafal Aydin, Berkay Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Over the past two decades, machine learning and deep learning techniques for forecasting solar flares have generated great impact due to their ability to learn from a high dimensional data space. However, lack of high quality data from flaring phenomena becomes a constraining factor for such tasks. One of the methods to tackle this complex problem is utilizing trained classifiers with multivariate time series of magnetic field parameters. In this work, we compare the exceedingly popular multivariate time series classifiers applying deep learning techniques with commonly used machine learning classifiers (i.e., SVM). We intend to explore the role of data augmentation on time series oriented flare prediction techniques, specifically the deep learning-based ones. We utilize four time series data augmentation techniques and couple them with selected multivariate time series classifiers to understand how each of them affects the outcome. In the end, we show that the deep learning algorithms as well as augmentation techniques improve our classifiers performance. the resulting classifiers' performance after augmentation outplayed the traditional flare forecasting techniques.

关键词： solar flare prediction deep neural networks data augmentation

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Unsupervised Feature Selection via Feature-Grouping and Orthogonal Constraint 26

Unsupervised Feature Selection via Feature-Grouping and Orth...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Yuan, Aihong Huang, Jiahao Wei, Chen Zhang, Wenjie Zhang, Naidan You, Mengbo Northwest A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the fields of machine learning and data mining, unsupervised feature selection plays an important role in processing large amounts of high-dimensional unlabeled data. this paper proposes an original and novel unsupervised feature selection based on feature grouping and orthogonal constraints. We consider the domain relationship in the original data and reconstruct the similarity matrix based on the correlation between the features. We use a generalized incoherent regression model based on orthogonal constraints. Furthermore, a graph regularization term with local structure preservation constraints is added to ensure that the feature subset does not lose local structural features in the original data space. Besides, an iterative algorithm is proposed to solve the optimization problem by iteratively updating the global similarity matrix, and constructing weight matrix, pseudo-label matrix and transformation matrix. through experiments on 6 benchmark datasets, the clustering performance of the proposed method outperforms state-of-the-art unsupervised feature selection methods. the source code is available at: https://***/misteru/FGOC.

关键词： Correlation Source coding machine learning Benchmark testing Feature extraction data structures data models

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Se²NAS: Self-Semi-Supervised architecture optimization for Semantic Segmentation 26

Se<SUP>2</SUP>NAS: Self-Semi-Supervised architecture optimiz...

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Pauletto, Loic Amini, Massih-Reza Winckler, Nicolas ATOS Grenoble France Univ Grenoble Alpes Grenoble France

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an optimized neural network (NN) model for this task by solving a jigsaw pretext problem identified by self-supervised learning over unlabeled training data, and, leveraging the structure of the unlabeled data with semi-supervised learning. Dynamic routing with a gradient descent approach is used to find the architecture of the NN model. Experiments on the Cityscapes and PASCAL VOC 2012 datasets show that the found neural network is four times more efficient than a state-of-the-art hand designed NN model in terms of floating-point operations.

关键词： Semantic segmentation Training data Artificial neural networks Self-supervised learning Semisupervised learning Search problems Routing

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FedGait: A Benchmark for Federated Gait recognition 26

FedGait: A Benchmark for Federated Gait Recognition

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Li, Ziqiong Li, Yan-ran Yu, Shiqi Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Gait recognition has been greatly improved by deep learning and can achieve a relative high accuracy. the advances depend on the data size of gait. However, due to public concerns on privacy and regulations and laws from different countries, it is very difficult and almost impossible to collect a huge centralized gait database for algorithm training. Federated learning is a distributed machine learning technique for privacy-preserving, and can help to solve the problem. We propose a federated gait recognition benchmark, FedGait, to train algorithms using distributed gait data. It is the first benchmark on gait recognition to the best of our knowledge. FedGait can utilizes the gait videos available on multiple clients to learn a robust and generalized model. Based on the real-world gait scenarios, we introduce two federated gait recognition scenarios: institution-based scenario (IBS) and device-based scenario (DBS). Compared with centralized training, federated learning will encounter more serious heterogeneous data and data imbalance problems. We employ four popular databases for experiments, CASIA-B, CASIA-E, ReSGait and OU-MVLP, are involved in FedGait to investigate the problems in federated learning. We hope FedGait is a good start to solve data privacy problem in gait recognition.

关键词： Training Privacy data privacy machine learning algorithms Federated learning Satellite broadcasting Benchmark testing

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Multi-Class Hypersphere Anomaly Detection 26

Multi-Class Hypersphere Anomaly Detection

引用

26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Kirchheim, Konstantin Filax, Marco Ortmeier, Frank Otto von Guericke Univ Dept Comp Sci Magdeburg Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

machine learning-based classification algorithms typically operate under assumptions that assert that the underlying data generating distribution is stationary and draws from a finite set of categories. In some scenarios, these assumptions might not hold, but identifying violating inputs - here referred to as anomalies - is a challenging task. Recent publications propose deep learning-based approaches that perform anomaly detection and classification jointly by (implicitly) learning a mapping that projects data points to a lower-dimensional space, such that the images of points of one class reside inside of a hypersphere, while others are mapped outside of it. In this work, we propose Multi-Class Hypersphere Anomaly Detection (MCHAD), a new hypersphere learning algorithm for anomaly detection in classification settings, as well as a generalization of existing hypersphere learning methods that allows incorporating example anomalies into the training. Extensive experiments on competitive benchmark tasks, as well as theoretical arguments, provide evidence for the effectiveness of our method. Our code is publicly available(1).

关键词： learning systems Training Codes Performance gain Benchmark testing Linear programming Classification algorithms

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Adv-Cut Paste: Semantic adversarial class specific data augmentation technique for object detection 26

Adv-Cut Paste: Semantic adversarial class specific data augm...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Arun, Kumar S. Pal, Abhijit Mopuri, Konda Reddy Gorthi, Rama Krishna Indian Inst Technol Tirupati Tirupati Andhra Pradesh India Indian Inst Technol Guwahati Gauhati India

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

data augmentation has been a prevalent approach in improving the performance of deep learning models against slight variations in data. Adversarial learning is one such form of data augmentation. In this work, we aim to introduce a framework to generate harder examples for a specific object class and an adversarial attack for the object detection task. We have also presented our study on the effect of training against such generated harder examples and adversarial samples in object detection. We have applied this adversarial learning technique to a YOLOv3 model and due to the nature of the attack, we demonstrated a substantial improvement in average precision (AP) for a single class of the COCO dataset. As per the literature, we are the first to introduce this kind of class-specific data augmentation strategy in object detection. With our approach, we have shown an improvement of 23.34% in AP for Cat class and 3.1% on overall mAP of YOLOv3 model on clean validation data, while 43.5% improvement in AP for the Cat class on the composite images with class-specific adversarial samples.

关键词： Training Deep learning Semantics Object detection data models Adversarial machine learning pattern recognition

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