2014 13th International Conference on Machine Learning and Applications最新文献_第10页

A Cyclic Contrastive Divergence Learning Algorithm for High-Order RBMs 一种高阶rbm的循环对比发散学习算法

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.18

D. Luo, Yi Wang, Xiaoqiang Han, Xihong Wu

{"title":"A Cyclic Contrastive Divergence Learning Algorithm for High-Order RBMs","authors":"D. Luo, Yi Wang, Xiaoqiang Han, Xihong Wu","doi":"10.1109/ICMLA.2014.18","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.18","url":null,"abstract":"The Restricted Boltzmann Machine (RBM), a special case of general Boltzmann Machines and a typical Probabilistic Graphical Models, has attracted much attention in recent years due to its powerful ability in extracting features and representing the distribution underlying the training data. A most commonly used algorithm in learning RBMs is called Contrastive Divergence (CD) proposed by Hinton, which starts a Markov chain at a data point and runs the chain for only a few iterations to get a low variance estimator. However, when referring to a high-order RBM, since there are interactions among its visible layers, the gradient approximation via CD learning usually becomes far from the log-likelihood gradient and even may cause CD learning to fall into an infinite loop with high reconstruction error. In this paper, a new algorithm named Cyclic Contrastive Divergence (CCD) is introduced for learning high-order RBMs. Unlike the standard CD algorithm, CCD updates the parameters according to each visible layer in turn, by borrowing the idea of Cyclic Block Coordinate Descent method. To evaluate the performance of the proposed CCD algorithm, regarding to high-order RBMs learning, both algorithms CCD and standard CD are theoretically analyzed, including convergence, estimate upper bound and both biases comparison, from which the superiority of CCD learning is revealed. Experiments on MNIST dataset for the handwritten digit classification task are performed. The experimental results show that CCD is more applicable and consistently outperforms the standard CD in both convergent speed and performance.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124084186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Activity Recognition Using Graphical Features 使用图形特征的活动识别

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.31

S. Akter, L. Holder

引用次数: 8

TSD: Detecting Sybil Accounts in Twitter TSD:在Twitter上检测Sybil帐户

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.81

Mansour Alsaleh, A. Alarifi, A. Al-Salman, Mohammed Alfayez, Abdulmajeed Almuhaysin

{"title":"TSD: Detecting Sybil Accounts in Twitter","authors":"Mansour Alsaleh, A. Alarifi, A. Al-Salman, Mohammed Alfayez, Abdulmajeed Almuhaysin","doi":"10.1109/ICMLA.2014.81","DOIUrl":"https://doi.org/10.1109/ICMLA.2014.81","url":null,"abstract":"Fake identities and user accounts (also called \"Sybils\") in online communities represent today a treasure for adversaries to spread fake product reviews, malware and spam on social networks, and Astroturf political campaigns. State-of-the-art in the defense mechanisms includes Automated Turing Tests (ATTs such as CAPTCHAs) and graph-based Sybil detectors. Sybil detectors in social networks leverage the assumption that Sybils will find it hard to befriend real users which leads to Sybils being connected to each other forming strongly connected sub graphs that can be detected using graph theory. However, the large majority of Sybils are in fact successful in integrating themselves into real user communities (such as the case in Twitter and Facebook). In this paper, we first study and compare the current detection mechanisms of Sybil accounts. We also explore various types of Twitter Sybil accounts detection features with the objective of building an effective and practical classifier. In order to build and evaluate our classifier, we collect and manually label a dataset of twitter accounts, including human users, bots, and hybrid (i.e., Tweets are posted by both human and bots). We believe this Twitter Sybils corpus will help researchers in conducting sound measurement studies. We also develop a browser plug-in (that we call Twitter Sybils Detector or TSD for short) that utilizes our classifier and warns the user about possible Sybil accounts before accessing them, upon clicking on a Twitter account.","PeriodicalId":109606,"journal":{"name":"2014 13th International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127809488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 50

Next Generation Application-Layer DDoS Defences: Applying the Concepts of Outlier Detection in Data Streams with Concept Drift 下一代应用层DDoS防御:在概念漂移数据流中应用离群检测的概念

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.80

D. Stevanovic, N. Vlajic

引用次数: 18

A Clustering-Based Grouping Model for Enhancing Collaborative Learning 基于聚类的协同学习分组模型

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.94

Yulei Pang, Feiya Xiao, Huaying Wang, Xiaozhen Xue

引用次数: 22

Ensemble Statistical and Heuristic Models for Unsupervised Word Alignment 无监督词对齐的集成统计和启发式模型

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.15

Mahsa Mohaghegh, Hossein Sarrafzadeh, M. Mohammadi

引用次数: 0

Semi-supervised Kernel-Based Temporal Clustering 半监督核时间聚类

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.25

Rodrigo Araujo, M. Kamel

引用次数: 7

State Abstraction in Reinforcement Learning by Eliminating Useless Dimensions 消除无用维度强化学习中的状态抽象

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.22

Zhao Cheng, L. Ray

引用次数: 1

Computation of a Rejection Threshold Used for the Bayes Classifier 贝叶斯分类器拒绝阈值的计算

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.61

M. Blankenburg, Christian Bloch, Jörg Krüger

引用次数: 1

Combining Exact and Metaheuristic Techniques for Learning Extended Finite-State Machines from Test Scenarios and Temporal Properties 结合精确和元启发式技术从测试场景和时间属性中学习扩展有限状态机

2014 13th International Conference on Machine Learning and Applications Pub Date : 2014-12-03 DOI: 10.1109/ICMLA.2014.62

D. Chivilikhin, V. Ulyantsev, A. Shalyto

引用次数: 9