检索结果-内蒙古大学图书馆

3rd IIAI international conference on Advanced Applied Informatics (IIAI-AAI)

作者： Matsumoto, Keiichi Yamasaki, Yuuki Matsumura, Yoshitaka Horibe, Noriko Ahrary, Alireza Aoqui, Shin-ichi Sojo Univ Grad Sch Engn Kumamoto Japan Sojo Univ Fac Comp & Informat Sci Kumamoto Japan i tex Corp Fukuoka Japan Sojo Univ Dept Comp & Informat Sci Kumamoto Japan

ISBN: (纸本)9781479941735

Since the number of farmers has been decreasing recently, shortage of the labor force is a serious problem in many farmhouses. In order to solve this problem, it is necessary to realize the system to support farmer's works in low costs. The purpose of our research is to construct the system which can predict the farmland environment in the near future. In this research, we focus on the control of soil wetness and temperature. We formalize a model for expressing the rule for predicting temperature and soil wetness from the latest environmental data of farmhouse. We show that the rule can be generated by the machine learning algorithm ID3. We research the confidence of each prediction by comparing data obtained from the experiment of cultivating farm products using a greenhouse. Based on the result, we research for finding environmental factors which are needed to create the hypothesis for the prediction of the environment transformation.

关键词： Agriculture data mining Decision tree

来源：评论

学校读者我要写书评

暂无评论

A Generalized Relationship mining Method for Social Media Text data

A Generalized Relationship Mining Method for Social Media Te...

引用

10th international conference on machine learning and data mining (MLDM)

作者： Sharma, Tuhin Toshniwal, Durga Indian Inst Technol Roorkee Dept Comp Sci & Engn Roorkee 247667 Uttar Pradesh India

ISBN: (纸本)9783319089799;9783319089782

Increasing popularity of Social Media has resulted in the creation of a huge amount of user generated documents. A large number of research works have focused on inferring relationship in certain specific social network domains. Few have considered structured data to establish syntax based relationship. In this work, we develop a two-step syntax based and semantic based relationship mining approach. Here we generalize the concept of relationship mining for all structured as well as unstructured unsupervised text documents from all social network domains. At first, we choose suitable features from individual document and store them in graph structure. Then we establish relationships in the graph generated to obtain Reduced node Social Graph with Relationships (RSGR). Our empirical study on various social media document validates the effectiveness of our approach and suggests its generality in finding relationships irrespective of the type of text documents and the social network domains.

关键词： Social network analysis Relationship mining Concept Wordnet Freebase Social graph Visualization

来源：评论

学校读者我要写书评

暂无评论

Simplifying rdF data for graph-based machine learning 3

Simplifying RDF data for graph-based machine learning

引用

3rd international Workshop on Knowledge Discovery and data mining Meets Linked Open data, Know@LOD 2014, Co-located with 11th Extended Semantic Web conference, ESWC 2014

作者： Bloem, Peter Wibisono, Adianto De Vries, Gerben K.D. System and Network Engineering Group Informatics Institute University of Amsterdam Netherlands Knowledge Representation and Reasoning Group Vrije Universiteit Amsterdam Netherlands

From the perspective of machine learning and data mining applications, expressing data in rdF rather than a domain-specific format can add complexity and obfuscate the internal structure. We investigate and illustrate this issue with an example where bio-molecular graph datasets are expressed in rdF. We use this example to inspire preprocessing techniques which reverse some of the complications of adding semantic annotations, exposing those patterns in the data that are most relevant to machine learning. We test these methods in a number of classification experiments and show that they can improve performance both for our example datasets and real-world rdF datasets.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

international conference on Artificial Intelligence and pattern recognition, AIPR 2014, Held at the 3rd World Congress on Computing and Information Technology, WCIT

International Conference on Artificial Intelligence and Patt...

引用

international conference on Artificial Intelligence and pattern recognition, AIPR 2014

The proceedings contain 25 papers. The topics discussed include: cloud and mobile security: challenges and future research directions;DLP-technologies: new directions and trends;using fuzzy logic to evaluate trust in e-commerce;gamification of teaching and learning activity: prospect and challenges of mobile game-based learning;ComboSplit: combining various splitting criteria for building a single decision tree;text classification using computational model of the cerebral cortex;restricted Boltzmann machines for modeling businesses;variables selection for multiclass SVM using the multiclass radius margin bound;on the enumeration of frequent patterns in sequences;predicting movie incomes using search engine query data;best-parameterized sigmoid ELM for benign and malignant breast cancer detection;inference engine for classification of expert systems using keyword extraction technique;comparison of classifiers for retinal pathology images using surf and bag-of-words model;content based video quality control for wide-area video surveillance systems;line detection by centre and width estimation;and interactive versus passive 2D face spoofing detection.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Covariance-guided One-Class Support Vector machine

引用

pattern recognition 2014年第6期47卷 2165-2177页

作者： Khan, Naimul Mefraz Ksantini, Riadh Ahmad, Imran Shafiq Guan, Ling Ryerson Univ Toronto ON M5B 2K3 Canada Univ Windsor Sch Comp Sci Fac Sci Windsor ON N9B 3P4 Canada Univ Windsor Sch Comp Sci Windsor ON N9B 3P4 Canada

In one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. Boundary-based methods like One-Class Support Vector machine (OSVM) preferentially separates the data from outliers along the large variance directions. On the other hand, retaining only the low variance directions can result in sacrificing some initial properties of the original data and is not desirable, specially in case of limited training samples. This paper introduces a Covariance-guided One-Class Support Vector machine (COSVM) classification method which emphasizes the low variance projectional directions of the training data without compromising any important characteristics. COSVM improves upon the OSVM method by controlling the direction of the separating hyperplane through incorporation of the estimated covariance matrix from the training data. Our proposed method is a convex optimization problem resulting in one global optimum solution which can be solved efficiently with the help of existing numerical methods. The method also keeps the principal structure of the OSVM method intact, and can be implemented easily with the existing OSVM libraries. Comparative experimental results with contemporary one-class classifiers on numerous artificial and benchmark datasets demonstrate that our method results in significantly better classification performance. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Covariance Support Vector machine One-class classification Outlier detection

来源：评论

学校读者我要写书评

暂无评论

Semi-supervised Time Series Modeling for Real-Time Flux Domain Detection on Passive DNS Traffic

Semi-supervised Time Series Modeling for Real-Time Flux Doma...

引用

10th international conference on machine learning and data mining (MLDM)

作者： Yu, Bin Smith, Les Threefoot, Mark Infoblox Inc Santa Clara CA 95054 USA

ISBN: (纸本)9783319089799;9783319089782

Flux domain is one of the most active threat vectors and its behavior keeps changing to evade existing detection measures. In order to differentiate the malicious flux domains from legitimate ones such as content delivery network (CDN) and network time protocol (NTP) services that have similar behavior, a novel time series model is created with a set of features that are not only focused on domain name system (DNS) time-to-live (TTL) but on loyalty and entropy of DNS resource records. An offline system is built with big data technology for training the model in a semi-supervised mode. In addition, an online platform is designed and developed to support large throughput real-time DNS streaming data processing with advanced analytics technologies. The feature extraction, classification, accuracy and performance are discussed based on large amount of real world DNS data in this paper.

关键词： Network security Semi-supervised machine learning Big data analytics Time series model DNS flux

来源：评论

学校读者我要写书评

暂无评论

Integrating Weight with Ensemble to Handle Changes in Class Distribution

Integrating Weight with Ensemble to Handle Changes in Class ...

引用

10th international conference on machine learning and data mining (MLDM)

作者： Limsetto, Nachai Waiyamai, Kitsana Kasetsart Univ Fac Engn Dept Comp Engn Bangkok 10900 Thailand

ISBN: (纸本)9783319089799;9783319089782

Concept drift can be considered as a distribution mismatch problem where class distribution changes as a time passes. This problem is commonly found in classification task of data mining. Among the proposed solutions, the cost-based Class Distribution Estimation (CDE) shows the best performance in coping with difference in class distribution between train and test datasets. However there is still some problem, as CDE lost its performance when there is too much change in class distribution. In this paper, CDE-weight is proposed to reduce the impact of high change in class distribution. The idea is to use many models suitable with many class distributions along with dynamic weighting method that adjusts weight of each model according to its class distribution. Experimented results indicate that CDE-Weight methods are able to reduce the impact of misestimating and improve the classifier performance when train and test data are different.

关键词： Concept drift Classification Cost-sensitive learning Quantification Class distribution estimation Ensemble method

来源：评论

学校读者我要写书评

暂无评论

Application and research of data mining in micro course platform construction

Application and research of data mining in micro course plat...

引用

3rd international conference on Energy and Environmental Protection, ICEEP 2014

作者： Xian, Jia Bao School of Computer Science Liaocheng University 252000 China

ISBN: (纸本)9783038351375

data mining can be used to make modeling for individual learner's usage record, combining with learner's basic information to make analysis of his habits, personal preferences to provide personalized service for the learner. At the same time by collecting and counting learners' recent access information in micro course platform to analyse the learning content, compare and match with mining pattern, and to sort according to the matching degree, forecasting the most possible knowledge for the learner in the next step, attaching sorting result to the end of the learner's requested page, for the learning content recommendation consequently, etc. Paper mainly introduced the specific application of data mining in micro course platform BBS. © (2014) Trans Tech Publications, Switzerland.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Incremental Ensemble Classifier Addressing Non-Stationary Fast data Streams 14

Incremental Ensemble Classifier Addressing Non-Stationary Fa...

引用

14th IEEE international conference on data mining (IEEE ICDM)

作者： Parker, Brandon S. Khan, Latifur Bifet, Albert Univ Texas Dallas 800 W Campbell Rd Richardson TX 75083 USA Huawei Noahs Ark Res Lab Unit 525 530 Shatin Hong Kong Peoples R China

ISBN: (纸本)9781479942749

Classification of data points in a data stream is a fundamentally different set of challenges than data mining on static data. While streaming data is often placed into the context of "Big data"(or more specifically "Fast data") wherein one-pass algorithms are used, true data streams offer additional hurdles due to their dynamic, evolving, and non-stationary nature. During the stream, the available labels (or concepts) often change, and a concept's definition in the feature space can also evolve (or drift) over time. The core issue is that the hidden generative function of the data is not a constant function, but rather evolves over time. This is known as a non-stationary distribution. In this paper, we describe a new approach to using ensembles for stream classification. While the core method is straightforward, it is specifically designed to adapt quickly with very little overhead to the dynamic and evolving nature of data streams generated from non-stationary functions. Our method, M-3, is based on a weighted majority ensemble of heterogeneous model types where model weights are updated on-line using Reinforcement learning techniques. We compare our method with current leading algorithms as implemented in the Massive Online Analysis (MOA) framework using UCI benchmark and synthetic stream generator data sets, and find that our method shows particularly strong gain over the baseline method when ground truth is of limited availability to the classifiers.

关键词： Big data data mining learning (artificial intelligence) pattern classification M3 method MOA framework UCI benchmark data generative function data point classification incremental ensemble classifier massive online analysis framework nonstationary distribution nonstationary fast data streams one-pass algorithms reinforcement learning techniques static data stream classification synthetic stream generator data sets weighted majority ensemble Accuracy data mining Equations Heuristic algorithms Prediction algorithms Training Training data Fast data Stream mining classifier non-stationary distribution

来源：评论

学校读者我要写书评

暂无评论

Image Vector Classification Algorithm for Hand-Writing Verification 3

Image Vector Classification Algorithm for Hand-Writing Verif...

引用

3rd international conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Singh, Tripty Mishra, Shivendra Amrita Vishwa Vidyapetham Sch Engn Bangalore Karnataka India

ISBN: (纸本)9781479930807

In this paper, we propose and implement the data mining techniques for verification of hand-writing recorded in an image. The captured images are considered independent of writing material in this system. This system consists of six sub-modules. Namely, i) Sample image data acquisition and preprocessing;ii) Vectors generation;iii) Computation of clusters;iv) Cluster Head Computation v) pattern Parameter Extraction;vi) Result. The first sub-module captures and categorizes the image for preprocessing. These preprocessed images are vectored and a cluster is computed based on thea) degree of entropy in the vectors. Therefore, these bunch of clusters represent themselves with the degree of entropy, type of cluster by choosing a cluster head. Finally, the parameters such as the distance, entropy, confidence, are extracted from the clustering;and a result is generated for the given set of samples.

关键词： Handwriting recognition Image Analysis Image Entropy Image Vectors pattern Analysis

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：