检索结果-内蒙古大学图书馆

22nd Pacific-Asia Conference on Advances in Knowledge Discovery and data Mining, PAKDD 2018

作者： Nguyen, Dang Nguyen, Tu Dinh Luo, Wei Venkatesh, Svetha Center for Pattern Recognition and Data Analytics School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9783319930398

Learning meaningful and effective representations for transaction data is a crucial prerequisite for transaction classification and clustering tasks. Traditional methods which use frequent itemsets (FIs) as features often suffer from the data sparsity and high-dimensionality problems. Several supervised methods based on discriminative FIs have been proposed to address these disadvantages, but they require transaction labels, thus rendering them inapplicable to real-world applications where labels are not given. In this paper, we propose an unsupervised method which learns low-dimensional continuous vectors for transactions based on information of both singleton items and FIs. We demonstrate the superior performance of our proposed method in classifying transactions on four datasets compared with several state-of-the-art baselines. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Geometric enclosing networks 27

Geometric enclosing networks

引用

27th International Joint Conference on Artificial Intelligence, IJCAI 2018

作者： Le, Trung Vu, Hung Nguyen, Tu Dinh Phung, Dinh Faculty of Information Technology Monash University Australia Center for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9780999241127

Training model to generate data has increasingly attracted research attention and become important in modern world applications. We propose in this paper a new geometry-based optimization approach to address this problem. Orthogonal to current state-of-the-art density-based approaches, most notably VAE and GAN, we present a fresh new idea that borrows the principle of minimal enclosing ball to train a generator G (z) in such a way that both training and generated data, after being mapped to the feature space, are enclosed in the same sphere. We develop theory to guarantee that the mapping is bijective so that its inverse from feature space to data space results in expressive nonlinear contours to describe the data manifold, hence ensuring data generated are also lying on the data manifold learned from training data. Our model enjoys a nice geometric interpretation, hence termed Geometric Enclosing Networks (GEN), and possesses some key advantages over its rivals, namely simple and easy-to-control optimization formulation, avoidance of mode collapsing and efficiently learn data manifold representation in a completely unsupervised manner. We conducted extensive experiments on synthesis and real-world datasets to illustrate the behaviors, strength and weakness of our proposed GEN, in particular its ability to handle multi-modal data and quality of generated data. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.

关键词： Geometry

来源：评论

学校读者我要写书评

暂无评论

Batch Normalized Deep Boltzmann Machines 10

Batch Normalized Deep Boltzmann Machines

引用

10th Asian Conference on Machine Learning, ACML 2018

作者： Vu, Hung Nguyen, Tu Dinh Le, Trung Luo, Wei Phung, Dinh Center for Pattern Recognition and Data Analytics Deakin University Geelong Australia Monash University ClaytonVIC3800 Australia

Training Deep Boltzmann Machines (DBMs) is a challenging task in deep generative model studies. The careless training usually leads to a divergence or a useless model. We discover that this phenomenon is due to the change of DBM layers’ input signals during model parameter updates, similar to other deterministic deep networks such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs). The change of layers’ input distributions not only complicates the learning process but also causes redundant neurons that simply imitate the others’ behaviors. Although this phenomenon can be coped using batch normalization in deep learning, integrating this technique into the probabilistic network of DBMs is a challenging problem since it has to satisfy two conditions of energy function and conditional probabilities. In this paper, we introduce Batch Normalized Deep Boltzmann Machines (BNDBMs) that meet both aforementioned conditions and successfully combine batch normalization and DBMs into the same framework. However, unlike CNNs, due to the probabilistic nature of DBMs, training DBMs with batch normalization has some differences: i) fixing shift parameters β but learning scale parameters γ;ii) avoiding normalizing the first hidden layer and iii) maintaining multiple pairs of population means and variances per neuron rather than one pair in CNNs. We observe that our proposed BNDBMs can stabilize the input signals of network layers and facilitate the training process as well as improve the model quality. More interestingly, BNDBMs can be trained successfully without pretraining, which is usually a mandatory step in most existing DBMs. The experimental results in MNIST, Fashion-MNIST and Caltech 101 Silhouette datasets show that our BNDBMs outperform DBMs and centered DBMs in terms of feature representation and classification accuracy (3.98% and 5.84% average improvement for pretraining and no pretraining respectively). © 2018 H. Vu, T.D. Nguyen, T. Le, W. Luo &

关键词： Network layers

来源：评论

学校读者我要写书评

暂无评论

Expected Hypervolume Improvement with Constraints

Expected Hypervolume Improvement with Constraints

引用

International Conference on pattern recognition

作者： Majid Abdolshah Alistair Shilton Santu Rana Sunil Gupta Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Australia

Bayesian optimisation has become a powerful framework for global optimisation of black-box functions that are expensive to evaluate and possibly noisy. In addition to expensive evaluation of objective functions, many real-world optimisation problems deal with similarly expensive black-box constraints. However, there are few studies regarding the role of constraints in multi-objective Bayesian optimisation. In this paper, we extend the Expected Hypervolume Improvement by introducing expectation of constraints satisfaction and merging them into a new acquisition function called Expected Hypervolume Improvement with Constraints (EHVIC). We analyse the performance of our algorithm by estimating the feasible region dominated by Pareto front using 4 benchmark functions. The proposed method is also evaluated on a realworld problem of Alloy Design. We demonstrate that EHVIC is an effective algorithm that provides a promising performance by comparing to a well-known related method.

关键词： Optimization Bayes methods Linear programming Benchmark testing Gaussian processes Evolutionary computation Frequency modulation

来源：评论

学校读者我要写书评

暂无评论

Latent sentiment topic modelling and nonparametric discovery of online mental health-related communities

引用

International Journal of data Science and analytics 2017年第3期4卷 209-231页

作者： Dao, Bo Nguyen, Thin Venkatesh, Svetha Phung, Dinh Centre for Pattern Recognition and Data Analytics Deakin University GeelongVIC3216 Australia

Social media are an online means of interaction among individuals. People are increasingly using social media, especially online communities, to discuss health concerns and seek support. Understanding topics, sentiment, and structures of these communities informs important aspects of health-related conditions. There has been growing research interest in analysing online mental health communities;however, analysis of these communities with health concerns has been limited. This paper investigates and identifies latent meta-groups of online communities with and without mental health-related conditions including depression and autism. Large datasets from online communities were crawled. We analyse sentiment-based, psycholinguistics-based and topic-based features from blog posts made by members of these online communities. The work focuses on using nonparametric methods to infer latent topics automatically from the corpus of affective words in the blog posts. The visualization of the discovered meta-communities in their use of latent topics shows a difference between the groups. This presents evidence of the emotion-bearing difference in online mental health-related communities, suggesting a possible angle for support and intervention. The methodology might offer potential machine learning techniques for research and practice in psychiatry. © 2017, Springer International Publishing AG.

关键词： Health

来源：评论

学校读者我要写书评

暂无评论

Algorithmic assurance: an active approach to algorithmic testing using Bayesian optimisation 18

Algorithmic assurance: an active approach to algorithmic tes...

引用

Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Shivapratap Gopakumar Sunil Gupta Santu Rana Vu Nguyen Svetha Venkatesh Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

We introduce algorithmic assurance, the problem of testing whether machine learning algorithms are conforming to their intended design goal. We address this problem by proposing an efficient framework for algorithmic testing. To provide assurance, we need to efficiently discover scenarios where an algorithm decision deviates maximally from its intended gold standard. We mathematically formulate this task as an optimisation problem of an expensive, black-box function. We use an active learning approach based on Bayesian optimisation to solve this optimisation problem. We extend this framework to algorithms with vector-valued outputs by making appropriate modification in Bayesian optimisation via the EXP3 algorithm. We theoretically analyse our methods for convergence. Using two real-world applications, we demonstrate the efficiency of our methods. The significance of our problem formulation and initial solutions is that it will serve as the foundation in assuring humans about machines making complex decisions.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Graph memory networks for molecular activity prediction

arXiv

引用

arXiv 2018年

作者： Pham, Trang Tran, Truyen Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia

Molecular activity prediction is critical in drug design. Machine learning techniques such as kernel methods and random forests have been successful for this task. These models require fixed-size feature vectors as input while the molecules are variable in size and structure. As a result, fixed-size fingerprint representation is poor in handling substructures for large molecules. Here we approach the problem through deep neural networks as they are flexible in modeling structured data such as grids, sequences and graphs. We train multiple BioAssays using a multi-task learning framework, which combines information from multiple sources to improve the performance of prediction, especially on small *** propose Graph Memory Network (GraphMem), a memory-augmented neural network to model the graph structure in molecules. GraphMem consists of a recurrent controller coupled with an external memory whose cells dynamically interact and change through a multi-hop reasoning process. Applied to the molecules, the dynamic interactions enable an iterative refinement of the representation of molecular graphs with multiple bond types. GraphMem is capable of jointly training on multiple datasets by using a specific-task query fed to the controller as an input. We demonstrate the effectiveness of the proposed model for separately and jointly training on more than 100K measurements, spanning across 9 BioAssay activity tests. Copyright © 2018, The Authors. All rights reserved.

关键词： Molecules

来源：评论

学校读者我要写书评

暂无评论

Nonparametric online machine learning with kernels 26

Nonparametric online machine learning with kernels

引用

26th International Joint Conference on Artificial Intelligence, IJCAI 2017

作者： Nguyen, Khanh Centre for Pattern Recognition and Data Analytics Deakin University Australia

ISBN: (纸本)9780999241103

Max-margin and kernel methods are dominant approaches to solve many tasks in machine learning. However, the paramount question is how to solve model selection problem in these methods. It becomes urgent in online learning context. Grid search is a common approach, but it turns out to be highly problematic in real-world applications. Our approach is to view max-margin and kernel methods under a Bayesian setting, then use Bayesian inference tools to learn model parameters and infer hyper-parameters in principle ways for both batch and online setting.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

A Kalman filtering induced heuristic optimization based partitional data clustering

arXiv

引用

arXiv 2019年

作者： Pakrashi, Arjun Chaudhuri, Bidyut B. Insight Centre for Data Analytics University College Dublin Ireland Computer Vision & Pattern Recognition Unit Indian Statistical Institute 203 B.T. Road Kolkata700108 India

Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heuristic approach called Heuristic Kalman Algorithm (HKA) has been proposed a few years ago, which may be used for optimizing an objective function in data/feature space. In this paper at first HKA is employed in partitional data clustering. Then an improved approach named HKA-K is proposed, which combines the benefits of global exploration of HKA and the fast convergence of K-Means method. Implemented and tested on several datasets from UCI machine learning repository, the results obtained by HKA-K were compared with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K is atleast as good as and often better than the other compared algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Dual discriminator generative adversarial nets 31

Dual discriminator generative adversarial nets

引用

31st Annual Conference on Neural Information Processing Systems, NIPS 2017

作者： Nguyen, Tu Dinh Le, Trung Vu, Hung Phung, Dinh Deakin University Centre for Pattern Recognition and Data Analytics Geelong Australia

We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN). Our idea is intuitive but proven to be very effective, especially in addressing some key limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and reverse KL divergences into a unified objective function, thus it exploits the complementary statistical properties from these divergences to effectively diversify the estimated density in capturing multi-modes. We term our method dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has two discriminators;and together with a generator, it also has the analogy of a minimax game, wherein a discriminator rewards high scores for samples from data distribution whilst another discriminator, conversely, favoring data from the generator, and the generator produces data to fool both two discriminators. We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem. We conduct extensive experiments on synthetic and real-world large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made our best effort to compare our D2GAN with the latest state-of-the-art GAN's variants in comprehensive qualitative and quantitative evaluations. The experimental results demonstrate the competitive and superior performance of our approach in generating good quality and diverse samples over baselines, and the capability of our method to scale up to ImageNet database. © 2017 Neural information processing systems foundation. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：