检索结果-内蒙古大学图书馆

European Conference on Mobile Robots (ECMR)

作者： Stefan Laible Yasir Niaz Khan Andreas Zell Chair of Cognitive Systems University of Tübingen Tübingen Germany Department of Machine Learning and Robotics University of Stuttgart Germany

For a mobile robot to navigate safely and efficiently in an outdoor environment, it has to recognize its surrounding terrain. Our robot is equipped with a low-resolution 3D LIDAR and a color camera. The data from both sensors are fused to classify the terrain in front of the robot. Therefore, the ground plane is divided into a grid and each cell is classified as either asphalt, cobblestones, grass or gravel. We use height and intensity features for the LIDAR data and Local ternary patterns for the image data. By additionally taking into account the context-sensitive nature of the terrain, the results can be improved significantly. We present a method based on Conditional Random Fields and compare it with a Markov Random Field based approach. We show that the Conditional Random Field model is better suited for our task. We achieve an average true positive rate of 94.2% for classifying the grid cells into the four terrain classes.

关键词： Laser radar Cameras Three-dimensional displays Image color analysis Robot sensing systems

来源：评论

学校读者我要写书评

暂无评论

Exploring friend's influence in cultures in Twitter

Exploring friend's influence in cultures in Twitter

引用

International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Anika Gupta Katia P. Sycara Geoffrey J. Gordon Ahmed Hefny Language Technologies Institute Carnegie Mellon University Robotics Institute Carnegie Mellon University Machine Learning Department Carnegie Mellon University

What does a user do when he logs in to the Twitter website? Does he merely browse through the tweets of all his friends as a source of information for his own tweets, or does he simply tweet a message of his own personal interest? Does he skim through the tweets of all his friends or only of a selected few? A number of factors might influence a user in these decisions. Does this social influence vary across cultures? In our work, we propose a simple yet effective model to predict the behavior of a user - in terms of which hashtag or named entity he might include in his future tweets. We have approached the problem as a classification task with the various influences contributing as features. Further, we analyze the contribution of the weights of the different features. Using our model we analyze data from different cultures and discover interesting differences in social influence.

关键词： Twitter History Computational modeling Analytical models Predictive models Conferences

来源：评论

学校读者我要写书评

暂无评论

learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition 13

Learning Hidden Markov Models from Non-sequence Data via Ten...

引用

Annual Conference on Neural Information Processing Systems

作者： Tzu-Kuo Huang Jeff Schneider Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 Robotics Institute Carnegie Mellon University Pittsburgh PA 15213

ISBN: (纸本)9781632660244

learning dynamic models from observed data has been a central issue in many scientific studies or engineering tasks. The usual setting is that data are collected sequentially from trajectories of some dynamical system operation. In quite a few modern scientific modeling tasks, however, it turns out that reliable sequential data are rather difficult to gather, whereas out-of-order snapshots are much easier to obtain. Examples include the modeling of galaxies, chronic diseases such Alzheimer's, or certain biological processes. Existing methods for learning dynamic model from non-sequence data are mostly based on Expectation-Maximization, which involves non-convex optimization and is thus hard to analyze. Inspired by recent advances in spectral learning methods, we propose to study this problem from a different perspective: moment matching and spectral decomposition. Under that framework, we identify reasonable assumptions on the generative process of non-sequence data, and propose learning algorithms based on the tensor decomposition method to provably recover first-order Markov models and hidden Markov models. To the best of our knowledge, this is the first formal guarantee on learning from non-sequence data. Preliminary simulation results confirm our theoretical findings.

关键词： nonsequence learning Convex function Biological Process Hidden Markov models spectral representation learning algorithms dynamic models Tensor

来源：评论

学校读者我要写书评

暂无评论

Maximum margin output coding

Maximum margin output coding

引用

29th International Conference on machine learning, ICML 2012

作者： Zhang, Yi Schneider, Jeff Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781450312851

In this paper we study output coding for multi-label prediction. For a multi-label output coding to be discriminative, it is important that codewords for different label vectors are significantly different from each other. In the meantime, unlike in traditional coding theory, codewords in output coding are to be predicted from the input, so it is also critical to have a predictable label encoding. To find output codes that are both discriminative and predictable, we first propose a max-margin formulation that naturally captures these two properties. We then convert it to a metric learning formulation, but with an exponentially large number of constraints as commonly encountered in structured prediction problems. Without a label structure for tractable inference, we use overgenerating (i.e., relaxation) techniques combined with the cutting plane method for optimization. In our empirical study, the proposed output coding scheme outperforms a variety of existing multi-label prediction methods for image, text and music classification. Copyright 2012 by the author(s)/owner(s).

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Direct robust matrix factorization for anomaly detection

Direct robust matrix factorization for anomaly detection

引用

11th IEEE International Conference on Data Mining, ICDM 2011

作者： Xiong, Liang Chen, Xi Schneider, Jeff Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9780769544083

Matrix factorization methods are extremely useful in many data mining tasks, yet their performances are often degraded by outliers. In this paper, we propose a novel robust matrix factorization algorithm that is insensitive to outliers. We directly formulate robust factorization as a matrix approximation problem with constraints on the rank of the matrix and the cardinality of the outlier set. Then, unlike existing methods that resort to convex relaxations, we solve this problem directly and efficiently. In addition, structural knowledge about the outliers can be incorporated to find outliers more effectively. We applied this method in anomaly detection tasks on various data sets. Empirical results show that this new algorithm is effective in robust modeling and anomaly detection, and our direct solution achieves superior performance over the state-of-the-art methods based on the L1-norm and the nuclear norm of matrices. © 2011 IEEE.

关键词： Matrix factorization

来源：评论

学校读者我要写书评

暂无评论

Group anomaly detection using Flexible Genre Models

Group anomaly detection using Flexible Genre Models

引用

25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

作者： Xiong, Liang Póczos, Barnabás Schneider, Jeff Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781618395993

An important task in exploring and analyzing real-world data sets is to detect unusual and interesting phenomena. In this paper, we study the group anomaly detection problem. Unlike traditional anomaly detection research that focuses on data points, our goal is to discover anomalous aggregated behaviors of groups of points. For this purpose, we propose the Flexible Genre Model (FGM). FGM is designed to characterize data groups at both the point level and the group level so as to detect various types of group anomalies. We evaluate the effectiveness of FGM on both synthetic and real data sets including images and turbulence data, and show that it is superior to existing approaches in detecting group anomalies.

关键词：

来源：评论

学校读者我要写书评

暂无评论

learning Auto-regressive models from sequence and non-sequence data

Learning Auto-regressive models from sequence and non-sequen...

引用

25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

作者： Huang, Tzu-Kuo Schneider, Jeff Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781618395993

Vector Auto-regressive models (VAR) are useful tools for analyzing time series data. In quite a few modern time series modelling tasks, the collection of reliable time series turns out to be a major challenge, either due to the slow progression of the dynamic process of interest, or inaccessibility of repetitive measurements of the same dynamic process over time. In those situations, however, we observe that it is often easier to collect a large amount of non-sequence samples, or snapshots of the dynamic process of interest. In this work, we assume a small amount of time series data are available, and propose methods to incorporate non-sequence data into penalized least-square estimation of VAR models. We consider non-sequence data as samples drawn from the stationary distribution of the underlying VAR model, and devise a novel penalization scheme based on the Lyapunov equation concerning the covariance of the stationary distribution. Experiments on synthetic and video data demonstrate the effectiveness of the proposed methods.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Hierarchical probabilistic models for group anomaly detection

Hierarchical probabilistic models for group anomaly detectio...

引用

14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011

作者： Xiong, Liang Póczos, Barnabás Schneider, Jeff Connolly, Andrew VanderPlas, Jake Machine Learning Department Carnegie Mellon University United States Robotics Institute Carnegie Mellon University United States Department of Astronomy University of Washington United States

Statistical anomaly detection typically focuses on finding individual point anomalies. Often the most interesting or unusual things in a data set are not odd individual points, but rather larger scale phenomena that only become apparent when groups of points are considered. In this paper, we propose generative models for detecting such group anomalies. We evaluate our methods on synthetic data as well as astronomical data from the Sloan Digital Sky Survey. The empirical results show that the proposed models are effective in detecting group anomalies. Copyright 2011 by the authors.

关键词： Artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

A reduction of imitation learning and structured prediction to no-regret online learning

A reduction of imitation learning and structured prediction ...

引用

14th International Conference on Artificial Intelligence and Statistics, AISTATS 2011

作者： Ross, Stéphane Gordon, Geoffrey J. Bagnell, J. Andrew Robotics Institute Carnegie Mellon University Pittsburgh PA 15213 United States Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches (Daumé III et al., 2009;Ross and Bagnell, 2010) provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem. Copyright 2011 by the authors.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Exploration in relational domains for model-based reinforcement learning

The Journal of Machine Learning Research

引用

The Journal of machine learning Research 2012年第1期13卷

作者： Tobias Lang Marc Toussaint Kristian Kersting Freie Universität Berlin Machine Learning and Robotics Group Berlin Germany Fraunhofer Institute for Intelligent Analysis and Information Systems Knowledge Discovery Department Sankt Augustin Germany

A fundamental problem in reinforcement learning is balancing exploration and exploitation. We address this problem in the context of model-based reinforcement learning in large stochastic relational domains by developing relational extensions of the concepts of the E3 and R-MAX algorithms. Efficient exploration in exponentially large state spaces needs to exploit the generalization of the learned model: what in a propositional setting would be considered a novel situation and worth exploration may in the relational setting be a well-known context in which exploitation is promising. To address this we introduce relational count functions which generalize the classical notion of state and action visitation counts. We provide guarantees on the exploration efficiency of our framework using count functions under the assumption that we had a relational KWIK learner and a near-optimal planner. We propose a concrete exploration algorithm which integrates a practically efficient probabilistic rule learner and a relational planner (for which there are no guarantees, however) and employs the contexts of learned relational rules as features to model the novelty of states and actions. Our results in noisy 3D simulated robot manipulation problems and in domains of the international planning competition demonstrate that our approach is more effective than existing propositional and factored exploration techniques.

关键词： exploration reinforcement learning relational transition models robotics statistical relational learning

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：