检索结果-内蒙古大学图书馆

ieee symposium on computational intelligence and data Mining (CIDM)

作者： Kumar, Anil Kumar, Nitesh Hussain, Muzammil Chaudhury, Santanu Agarwal, Sumeet Samsung R&D Inst SRI Delhi Noida India IIT Delhi Dept Elect Engn Delhi India

ISBN: (纸本)9781479945184

Cross-domain recommendation systems exploit tags, textual descriptions or ratings available for items in one domain to recommend items in multiple domains. Handling unstructured/unannotated item information is, however, a challenge. Topic modeling offer a popular method for deducing structure in such data corpora. In this paper, we introduce the concept of a common latent semantic space, spanning multiple domains, using topic modeling of semantic clustered vocabularies of distinct domains. The intuition here is to use explicitly-determined semantic relationships between non-identical, but possibly semantically equivalent, words in multiple domain vocabularies, in order to capture relationships across information obtained in distinct domains. The popular WordNet based ontology is used to measure semantic relatedness between textual words. The experimental results shows that there is a marked improvement in the precision of predicting user preferences for items in one domain when given the preferences in another domain.

关键词： ontologies (artificial intelligence) pattern clustering recommender systems semantic networks WordNet based ontology cross-domain recommendation systems data corpora explicitly-determined semantic relationship latent semantic space multiple domain vocabulary semantic clustered vocabulary semantic clustering-based cross-domain recommendation semantic relatedness spanning multiple domain textual word topic modeling unstructured/unannotated item information user preference Clustering algorithms Collaboration data models Ontologies Resource management Semantics Vocabulary User preferences Semantics Recommender systems Ontology semantic networks data models Clustering algorithms Vocabulary management of resources Cluster Analysis

来源：评论

学校读者我要写书评

暂无评论

Valid Interpretation of Feature Relevance for Linear data Mappings 5

Valid Interpretation of Feature Relevance for Linear Data Ma...

引用

ieee symposium on computational intelligence and data Mining (CIDM)

作者： Frenay, Benoit Hofmann, Daniela Schulz, Alexander Biehl, Michael Hammer, Barbara Univ Bielefeld CITEC Ctr Excellence D-33615 Bielefeld Germany Catholic Univ Louvain ICTEAM Inst Machine Learning Grp Pl Levant 3 B-1348 Louvain La Neuve Belgium Univ Groningen Math & Comp Sci NL-9700 AK Groningen Netherlands

ISBN: (纸本)9781479945184

Linear data transformations constitute essential operations in various machine learning algorithms, ranging from linear regression up to adaptive metric transformation. Often, linear scalings are not only used to improve the model accuracy, rather feature coefficients as provided by the mapping are interpreted as an indicator for the relevance of the feature for the task at hand. This principle, however, can be misleading in particular for high-dimensional or correlated features, since it easily marks irrelevant features as relevant or vice versa. In this contribution, we propose a mathematical formalisation of the minimum and maximum feature relevance for a given linear transformation which can efficiently be solved by means of linear programming. We evaluate the method in several benchmarks, where it becomes apparent that the minimum and maximum relevance closely resembles what is often referred to as weak and strong relevance of the features;hence unlike the mere scaling provided by the linear mapping, it ensures valid interpretability.

关键词： data handling learning (artificial intelligence) linear programming regression analysis adaptive metric transformation linear data mappings linear data transformations linear regression machine learning algorithms mathematical formalisation maximum feature relevance minimum feature relevance Accuracy Biological system modeling data models Eigenvalues and eigenfunctions Niobium Upper bound Vectors linear programming Machine learning algorithms Biological system modeling data models data processing Eigenfunctions Linear Regression Upper bound niobium regression analysis MAPPING(MATHEMATICAL)

来源：评论

学校读者我要写书评

暂无评论

big data Topic Modeling with Mahout for Managing Business Analysis Services 7

Big Data Topic Modeling with Mahout for Managing Business An...

引用

ieee/SICE International symposium on System Integration (SII)

作者： Romsaiyud, Walisa Siam Univ Grad Sch Informat Technol Bangkok 10160 Thailand

ISBN: (纸本)9781479969449

Topic modeling for big data provides a key opportunity to address the needs of data-driven businesses in a way to deliver genuine value to business users simplifying search and summary processes via the vast amount of information. Many businesses have already worked with Hadoop paradigm in order to rapidly apply computational processing to merge data from several operational systems and analyze large volumes of multi-structured data. In this paper, we extended the features of collapsed variational Bayesian (CVB) inference algorithm for Latent Dirichlet Allocation (LDA) to discover the hidden topical patterns through statistical regularities and eliminate noises on Hadoop framework. The approach captures the evolution of topics in a sequentially organized corpus of documents into two mainly phases, mapping and reducing phases. In the mapping phase the probabilistic on each word, in collected documents, is calculated by using collapsed space of latent variables and parameters for summarizing words in each topic, and reducing phase to utilize the various results from map phase while predicting a new topic model from a given trained models. The study conducts the experiments based on a Reuters-21578 text categorization collection corpus on Hadoop clustering with 64 nodes to improve the computationally in a more efficient and accurate approach.

关键词： big data belief networks business data processing distributed processing document handling inference mechanisms pattern clustering CVB Hadoop clustering Hadoop framework Hadoop paradigm LDA Mahout Reuters-21578 text categorization collection corpus big data topic modeling business analysis service management collapsed variational Bayesian inference algorithm computational processing data-driven businesses hidden topical patterns latent dirichlet allocation map phase multistructured data operational systems sequentially organized documents corpus statistical regularities Analytical models Bayes methods data models Hidden Markov models Inference algorithms Probabilistic logic Text categorization big data Inference algorithms electronic document handling Text categorization PVB protocol business data processing Inference mechanisms Probabilistic logic Bayesian networks data models Distributed processing Bayes methods Hidden Markov models laser Doppler anemometry Anterior interventricular branch of left coronary artery

来源：评论

学校读者我要写书评

暂无评论

Optimal Capacity Allocation for executing MapReduce Jobs in Cloud Systems 16

Optimal Capacity Allocation for executing MapReduce Jobs in ...

引用

16th International symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Malekimajd, M. Rizzi, A. M. Ardagna, D. Ciavotta, M. Passacantando, M. Movaghar, A. Sharif Univ Technol Dept Comp Engn Tehran Iran Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy Univ Pisa Dipartimento Informat Pisa Italy

ISBN: (纸本)9781479984480

Nowadays, analyzing large amount of data is of paramount importance for many companies. big data and business intelligence applications are facilitated by the MapReduce programming model while, at infrastructural layer, cloud computing provides flexible and cost effective solutions for allocating on demand large clusters. Capacity allocation in such systems is a key challenge to providing performance for MapReduce jobs and minimize cloud resource cost. The contribution of this paper is twofold: (i) we formulate a linear programming model able to minimize cloud resources cost and job rejection penalties for the execution of jobs of multiple classes with (soft) deadline guarantees, (ii) we provide new upper and lower bounds for MapReduce job execution time in shared Hadoop clusters. Moreover, our solutions are validated by a large set of experiments. We demonstrate that our method is able to determine the global optimal solution for systems including up to 1000 user classes in less than 0.5 seconds. Moreover, the execution time of MapReduce jobs are within 19% of our upper bounds on average.

关键词： big data cloud computing data analysis linear programming parallel programming pattern clustering resource allocation MapReduce job execution time MapReduce programming model big data applications business intelligence applications capacity allocation cloud resource cost minimization cloud systems infrastructural layer job rejection linear programming model optimal capacity allocation shared Hadoop clusters Bismuth Mathematical model Optimization Resource management Scalability Silicon Upper bound Capacity Allocation Cloud Computing MapReduce Performance bounds Cloud Computing big data linear programming models management of resources Upper bound jobs Parallel programming data analysis Scalability bismuth Cluster Analysis linear programming Resource utilization Mathematical Model big data applications

来源：评论

学校读者我要写书评

暂无评论

Wolf Search Algorithm for Attribute Reduction in classification 5

Wolf Search Algorithm for Attribute Reduction in classificat...

引用

ieee symposium on computational intelligence and data Mining (CIDM)

作者： Yamany, Waleed Emary, E. Hassanien, Aboul Ella Fayoum Univ Fac Comp & Informat Al Fayyum Egypt Cairo Univ Fac Comp & Informat Cairo Egypt SRGE Cairo Egypt

ISBN: (纸本)9781479945184

data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might minimize the classification accuracy because of the huge search space. The main goal of attribute reduction is choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy than using all attributes. A system for feature selection is proposed in this paper using a modified version of the wolf search algorithm optimization. WSA is a bio-inspired heuristic optimization algorithm that imitates the way wolves search for food and survive by avoiding their enemies. The WSA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system is applied on a set of the UCI machine learning data sets and proves good performance in comparison with the GA and PSO optimizers commonly used in this context.

关键词： data handling feature selection genetic algorithms learning (artificial intelligence) particle swarm optimisation search problems GA PSO optimizers UCI machine learning data sets WSA attribute reduction bio-inspired heuristic optimization algorithm classification accuracy feature reduction size feature space fitness function huge search space irrelevant attributes near-optimal feature subset redundant attributes wolf search algorithm optimization Accuracy Equations Optimization Sociology Standards Training Visualization Feature extraction Eigenvector tabu search data processing Particle swarm optimization Sociology Gallium Genetic Algorithm (GA) Visualization wolf Redundant Functional training SEARCHING ALGORITHM

来源：评论

学校读者我要写书评

暂无评论

Multivariate PDF Matching via Kernel Density Estimation

Multivariate PDF Matching via Kernel Density Estimation

引用

ieee symposium on computational intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)

作者： Fantinato, Denis G. Boccato, Levy Attux, Romis Neves, Aline Univ Estadual Campinas Sch Elect & Comp Engn Campinas SP Brazil Fed Univ ABC Engn Modeling & Appl Social Sci Ctr Sao Andre SP Brazil

ISBN: (纸本)9781479945030

In this work, a measure of similarity based on the matching of multivariate probability density functions (PDFs) is proposed. In consonance with the information theoretic learning (ITL) framework, the affinity comparison between the joint PDFs is performed using a quadratic distance, estimated with the aid of the Parzen window method with Gaussian kernels. The motivation underlying this proposal is to introduce a criterion capable of quantifying, to a significant extent, the statistical dependence present on information sources endowed with temporal and/or spatial structure, like audio, images and coded data. The measure is analyzed and compared with the canonical ITL-based approach - correntropy - for a set of blind equalization scenarios. The comparison includes elements like surface analysis, performance comparison in terms of bit error rate and a qualitative discussion concerning image processing. It is also important to remark that the study includes the application of two computational intelligence paradigms: extreme learning machines and differential evolution. The results indicate that the proposal can be, in some scenarios, a more informative formulation than correntropy.

关键词： Gaussian processes evolutionary computation image processing learning (artificial intelligence) probability Gaussian kernel ITL framework Parzen window method blind equalization scenario computational intelligence paradigm correntropy approach differential evolution extreme learning machines information theoretic learning framework kernel density estimation multivariate PDF matching probability density function quadratic distance statistical dependence computational efficiency Estimation Joints Kernel Measurement Probability density function Vectors

来源：评论

学校读者我要写书评

暂无评论

Using supervised training signals of observable state dynamics to speed-up and improve reinforcement learning

Using supervised training signals of observable state dynami...

引用

ieee symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL)

作者： Elliott, Daniel L. Anderson, Charles Colorado State Univ Dept Comp Sci Ft Collins CO 80523 USA

ISBN: (纸本)9781479945528

A common complaint about reinforcement learning (RL) is that it is too slow to learn a value function which gives good performance. This issue is exacerbated in continuous state spaces. This paper presents a straight-forward approach to speeding-up and even improving RL solutions by reusing features learned during a pre-training phase prior to Q-learning. During pre-training, the agent is taught to predict state change given a state/action pair. The effect of pre-training is examined using the model-free Q-learning approach but could readily be applied to a number of RL approaches including model-based RL. The analysis of the results provides ample evidence that the features learned during pre-training is the reason behind the improved RL performance.

关键词： learning (artificial intelligence) neural nets state-space methods RL performance improvement RL solution improvement continuous state spaces feature reuse model-based RL approach model-free Q-learning approach observable state dynamics pretraining phase reinforcement learning state change prediction state-action pair supervised training signals value function learning Artificial neural networks computational modeling data models Heuristic algorithms Learning (artificial intelligence) Supervised learning Training learning (artificial intelligence) State-space methods data models Neural network Semi-supervised Learning Heuristic algorithms computational modeling Artificial neural networks Functional training Learning State Change Training

来源：评论

学校读者我要写书评

暂无评论

Path-finding Using Reinforcement Learning and Affective States 23

Path-finding Using Reinforcement Learning and Affective Stat...

引用

23rd ieee International symposium on Robot and Human Interactive Communication (ieee RO-MAN)

作者： Feldmaier, Johannes Diepold, Klaus Tech Univ Munich Inst Data Proc Dept Elect Engn D-80290 Munich Germany

ISBN: (纸本)9781479967650

During decision making and acting in the environment humans appraise decisions and observations with feelings and emotions. In this paper we propose a framework to incorporate an emotional model into the decision making process of a machine learning agent. We use a hierarchical structure to combine reinforcement learning with a dimensional emotional model. The dimensional model calculates two dimensions representing the actual affective state of the autonomous agent. For the evaluation of this combination, we use a reinforcement learning experiment (called Dyna Maze) in which, the agent has to find an optimal path through a maze. Our first results show that the agent is able to appraise the situation in terms of emotions and react according to them.

关键词： computational modeling Decision making Learning (artificial intelligence) Planning Psychology Robots Emotions learning (artificial intelligence) computational modeling Psychology Robot decision making Agents Intelligent agents Mazes optimal path decision making process Learning Planning hierarchical structure Feelings

来源：评论

学校读者我要写书评

暂无评论

Reports of the 2014 AAAI Spring symposium Series

引用

AI MAGAZINE 2014年第3期35卷 70-76页

作者： Jain, Manish Jiang, Albert Xin Kido, Takashi Takadama, Keiki Mercer, Eric G. Rungta, Neha Waser, Mark Wagner, Alan Burke, Jennifer Sofge, Don Lawless, William Sridharan, Mohan Hawes, Nick Hwang, Tim Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Rikengenesis Yokohama Kanagawa Japan Univ Electrocommun Chofu Tokyo 182 Japan Brigham Young Univ Dept Comp Sci Provo UT 84602 USA NASA Ames Res Ctr Moffett Field CA USA Digital Wisdom Inst New York NY USA Georgia Inst Technol Georgia Tech Res Inst Atlanta GA 30332 USA Boeing Res & Technol New York NY USA Naval Res Lab Washington DC USA Pain Coll Augusta GA USA Texas Tech Univ Lubbock TX 79409 USA Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Pacific Social Architecting Corp New York NY USA Data & Soc Res Inst New York City New York NY USA

The Association for the Advancement of Artificial intelligence was pleased to present the AAAI 2014 Spring symposium Series, held Monday through Wednesday, March 24-26, 2014. The titles of the eight symposia were Applied computational Game Theory, big data Becomes Personal:, Knowledge into Meaning, Formal Verification and Modeling in Human-Machine Systems, Implementing Selves with Safe Motivational Systems and Self-Improvement, The Intersection of Robust intelligence and Trust in Autonomous Systems, Knowledge Representation and Reasoning in Robotics, Qualitative Representations for Robots, and Social Hacking and Cognitive Security on the Internet and New Media). This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Some Results and Algorithm for One-sided Concept Lattices with Separable Modifiers

Some Results and Algorithm for One-sided Concept Lattices wi...

引用

9th ieee International symposium on Applied computational intelligence and Informatics (SACI)

作者： Butka, P. Pocsova, J. Pocs, J. Tech Univ Kosice Fac Elect Engn & Informat Dept Cybernet & Artificial Intelligence Kosice Slovakia Tech Univ Kosice BERG Fac Inst Control & Informat Product Proc Kosice Slovakia Palacky Univ Dept Algebra & Geometry Olomouc Czech Republic Slovak Acad Sci Math Inst Kosice Slovakia

ISBN: (纸本)9781479946945

In this paper we provide a modification of one-sided concept lattices based on the usage of fuzzy separable modifiers. The proposed method is motivated by so-called fuzzy concept lattices with hedges and represents their slight modification. In fuzzy set theory, modifiers are often defined within the framework of linguistic variables, representing adverbs, while one-sided concept lattices provides the specific fuzzy version of data analytical method based on the approach known as Formal Concept Analysis. We define concept forming operators based on separable modifiers and we show that these operators form a Galois connection between power set of objects and fuzzy subsets of attributes. An algorithm for a creation of one-sided concept lattices with separable modifiers is also presented.

关键词： Concept Lattice Modifiers Modifier Genes Separable Fuzzy Adverbs hedges

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：