检索结果-内蒙古大学图书馆

Partitional Clustering of Protein Sequences - An inductive logic programming Approach

10th International Work-Conference on Artificial Neural Networks (IWANN 2009)

作者： Fonseca, Nuno A. Costa, Vitor S. Camacho, Rui Vieira, Cristina Vieira, Jorge Univ Porto IBMC Rua Campo Alegre 823 P-4150180 Oporto Portugal Univ Porto INESC Porto LA CRACS Porto Portugal Univ Porto LIAAD INSEC Porto LA & FEUP Porto Portugal

ISBN: (纸本)9783642024801

We present a novel approach to cluster sets of protein sequences, based on inductive logic programming (ILP). Preliminary results show that;the method proposed Produces understand able descriptions/explanations of the clusters. Furthermore, it can be used as a knowledge elicitation tool to explain clusters proposed by other clustering approaches, such as standard phylogenetic programs.

关键词： Clustering inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

An integrated ILP model for Routing, Modulation Level and Spectrum Allocation in the next generation DCN

An integrated ILP model for Routing, Modulation Level and Sp...

引用

Third International Conference on Cyberspace Technology (CCT 2015)

作者： Xiao Jia Fan Ning Shan Yin Dajiang Wang Jie Zhang Shanguo Huang State Key Laboratory of Information Photonics and Optical Communication Beijing University of Posts and Telecommunications 100876 China Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications 100876 China ZTE Corporation Beijing China

This paper focus on the routing, modulation level and spectrum allocation (RMLSA) problem in the next generation datacentre networks, which own the advantages of both Elastic Optical Networks (EON) and Software Defined Networking (SDN). We proposes an Integer Linear programming (ILP) model for this problem. Simulation results show the ILP model achieves an outstanding load balancing performance.

关键词： Datacenter Networks Integer Linear programming model Routing Modulation Level and Spectrum Allocation Spectrum allocation inductive logic programming linear programming models Network routing

来源：评论

学校读者我要写书评

暂无评论

TEMPLATE BASED INTER-LAYER PREDICTION FOR HIGH DYNAMIC RANGE SCALABLE COMPRESSION

TEMPLATE BASED INTER-LAYER PREDICTION FOR HIGH DYNAMIC RANGE...

引用

IEEE International Conference on Image Processing

作者： Mikael Le Pendu Christine Guillemot Dominique Thoreau INRIA Technicolor

ISBN: (纸本)9781479983407

This paper presents a scalable high dynamic range (HDR) image coding framework in which the base layer is a low dynamic range (LDR) version of the image that may have been generated by an arbitrary Tone Mapping Operator (TMO). Our method successfully handles the case of complex local TMOs thanks to a block-wise and non-linear approach. A novel template based Inter Layer Prediction (ILP) is designed in order to perform the inverse tone mapping of a block without the need to transmit any additional parameter to the decoder. This method enables the use of a more accurate inverse tone mapping model than the simple linear regression commonly used for block-wise ILP. Our experiments have shown an average bitrate saving of 34% on the HDR enhancement layer, compared to state of the art methods.

关键词： High Dynamic Range (HDR) Tone Mapping Inverse Tone mapping Scalability HEVC Inter Layer Prediction (ILP) Scalability jigs(templates) High efficiency video coding HIGH DYNAMIC RANGE inductive logic programming BASE COURSE GATA3 gene hot dry rocks Dynamic range MAPPING(MATHEMATICAL)

来源：评论

学校读者我要写书评

暂无评论

Plane-based object categorisation using relational learning

引用

MACHINE LEARNING 2014年第1期94卷 3-23页

作者： Farid, Reza Sammut, Claude Univ New S Wales Sch Comp Sci & Engn Sydney NSW 2052 Australia

We use inductive logic programming (ILP) to learn classifiers for generic object recognition from point clouds, as generated by 3D cameras, such as the Kinect. Each point cloud is segmented into planar surfaces. Each subset of planes that represents an object is labelled and predicates describing those planes and their relationships are used for learning. Our claim is that a relational description for classes of 3D objects can be built for robust object categorisation in real robotic application. To test the hypothesis, labelled sets of planes from 3D point clouds gathered during the RoboCup Rescue Robot competition are used as positive and negative examples for an ILP system. The robustness of the results is evaluated by 10-fold cross validation. In addition, common household objects that have curved surfaces are used for evaluation and comparison against a well-known non-relational classifier. The results show that ILP can be successfully applied to recognise objects encountered by a robot especially in an urban search and rescue environment.

关键词： Object classification inductive logic programming Machine learning Urban search and rescue 3D point cloud

来源：评论

学校读者我要写书评

暂无评论

Relational machine learning for electronic health record-driven phenotyping

引用

JOURNAL OF BIOMEDICAL INFORMATICS 2014年 52卷 260-270页

作者： Peissig, Peggy L. Costa, Vitor Santos Caldwell, Michael D. Rottscheit, Carla Berg, Richard L. Mendonca, Eneida A. Page, David Marshfield Clin Res Fdn Biomed Informat Res Ctr Marshfield WI 54449 USA Univ Porto DCC FCUP P-4100 Oporto Portugal Univ Porto Dept Ciencia Comp CRACS INESC TEC P-4100 Oporto Portugal Marshfield Clin Fdn Med Res & Educ Dept Surg Marshfield WI USA Univ Wisconsin Dept Biostat & Med Informat Madison WI 53706 USA Univ Wisconsin Dept Pediat Madison WI 53706 USA Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

Objective: Electronic health records (EHR) offer medical and pharmacogenomics research unprecedented opportunities to identify and classify patients at risk. EHRs are collections of highly inter-dependent records that include biological, anatomical, physiological, and behavioral observations. They comprise a patient's clinical phenome, where each patient has thousands of date-stamped records distributed across many relational tables. Development of EHR computer-based phenotyping algorithms require time and medical insight from clinical experts, who most often can only review a small patient subset representative of the total EHR records, to identify phenotype features. In this research we evaluate whether relational machine learning (ML) using inductive logic programming (ILP) can contribute to addressing these issues as a viable approach for EHR-based phenotyping. Methods: Two relational learning ILP approaches and three well-known WEKA (Waikato Environment for Knowledge Analysis) implementations of non-relational approaches (PART, J48, and JRIP) were used to develop models for nine phenotypes. International Classification of Diseases, Ninth Revision (ICD-9) coded EHR data were used to select training cohorts for the development of each phenotypic model. Accuracy, precision, recall, F-Measure, and Area Under the Receiver Operating Characteristic (AUROC) curve statistics were measured for each phenotypic model based on independent manually verified test cohorts. A two-sided binomial distribution test (sign test) compared the five ML approaches across phenotypes for statistical significance. Results: We developed an approach to automatically label training examples using ICD-9 diagnosis codes for the ML approaches being evaluated. Nine phenotypic models for each ML approach were evaluated, resulting in better overall model performance in AUROC using ILP when compared to PART (p = 0.039), J48 (p = 0.003) and JRIP (p = 0.003). Discussion: ILP has the potential to impro

关键词： Machine learning Electronic health record inductive logic programming Phenotyping Relational machine learning

来源：评论

学校读者我要写书评

暂无评论

Learning from interpretation transition

引用

MACHINE LEARNING 2014年第1期94卷 51-79页

作者： Inoue, Katsumi Ribeiro, Tony Sakama, Chiaki Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan Grad Univ Adv Studies Sokendai Dept Informat Chiyoda Ku Tokyo 1018430 Japan Wakayama Univ Dept Comp & Commun Sci Wakayama 6408510 Japan

We propose a novel framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I,J) such that J=T (P) (I), where T (P) is the immediate consequence operator, we infer the program P. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata.

关键词： Dynamical systems Boolean networks Cellular automata Attractors Supported models Learning from interpretation inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

Finding relational redescriptions

引用

MACHINE LEARNING 2014年第3期96卷 225-248页

作者： Galbrun, Esther Kimmig, Angelika Univ Helsinki Dept Comp Sci Helsinki 00014 Finland Univ Helsinki Helsinki Inst Informat Technol FIN-00014 Helsinki Finland Katholieke Univ Leuven Dept Computerwetenschappen B-3001 Heverlee Belgium

We introduce relational redescription mining, that is, the task of finding two structurally different patterns that describe nearly the same set of object pairs in a relational dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. We propose an alternating scheme for solving this problem. Its core consists of a novel relational query miner that efficiently identifies discriminative connection patterns between pairs of objects. Compared to a baseline inductive logic programming (ILP) approach, our query miner is able to mine more complex queries, much faster. We performed extensive experiments on three real world relational datasets, and present examples of redescriptions found, exhibiting the power of the method to expressively capture relations present in these networks.

关键词： Redescription mining Relational query mining inductive logic programming Graph mining Relational data mining

来源：评论

学校读者我要写书评

暂无评论

Improving Hit Ratio of ILP-based Concept Discovery System with Memoization

引用

COMPUTER JOURNAL 2014年第1期57卷 138-153页

作者： Mutlu, Alev Senkul, Pinar Middle E Tech Univ Dept Comp Engn TR-06531 Ankara Turkey

Although inductive logic programming (ILP)-based concept discovery systems have applications in a wide range of domains, they still suffer from scalability and efficiency issues. One of the reasons for the efficiency problem is the high number of query executions necessary in the concept discovery process. Owing to the refinement operator of ILP-based concept discovery systems, these queries repeat frequently. In this work, we propose a method to improve the look-up table hit ratio for repeating queries of ILP-based concept discovery systems with memoization capabilities. The proposed method introduces modifications on search space evaluation and the covering steps of such systems so that query results of the previous iterations can be exploited. Experimental results show that the proposed method improves the hash table hit ratio of ILP-based concept discovery systems with an affordable cost of extra memory consumption.

关键词： inductive logic programming concept discovery efficiency memoization hit count

来源：评论

学校读者我要写书评

暂无评论

Meta-interpretive learning: application to grammatical inference

引用

MACHINE LEARNING 2014年第1期94卷 25-49页

作者： Muggleton, Stephen H. Lin, Dianhuan Pahlavi, Niels Tamaddoni-Nezhad, Alireza Univ London Imperial Coll Sci Technol & Med Dept Comp London England

Despite early interest Predicate Invention has lately been under-explored within ILP. We develop a framework in which predicate invention and recursive generalisations are implemented using abduction with respect to a meta-interpreter. The approach is based on a previously unexplored case of Inverse Entailment for Grammatical Inference of Regular languages. Every abduced grammar H is represented by a conjunction of existentially quantified atomic formulae. Thus Anot signH is a universally quantified clause representing a denial. The hypothesis space of solutions for Anot signH can be ordered by theta-subsumption. We show that the representation can be mapped to a fragment of Higher-Order Datalog in which atomic formulae in H are projections of first-order definite clause grammar rules and the existentially quantified variables are projections of first-order predicate symbols. This allows predicate invention to be effected by the introduction of first-order variables. Previous work by Inoue and Furukawa used abduction and meta-level reasoning to invent predicates representing propositions. By contrast, the present paper uses abduction with a meta-interpretive framework to invent relations. We describe the implementations of Meta-interpretive Learning (MIL) using two different declarative representations: Prolog and Answer Set programming (ASP). We compare these implementations against a state-of-the-art ILP system MC-TopLog using the dataset of learning Regular and Context-Free grammars as well learning a simplified natural language grammar and a grammatical description of a staircase. Experiments indicate that on randomly chosen grammars, the two implementations have significantly higher accuracies than MC-TopLog. In terms of running time, Metagol is overall fastest in these tasks. Experiments indicate that the Prolog implementation is competitive with the ASP one due to its ability to encode a strong procedural bias. We demonstrate that MIL can be applied to learni

关键词： inductive logic programming Meta-interpretative learning Predicate invention Recursion Grammatical inference

来源：评论

学校读者我要写书评

暂无评论

Fast relational learning using bottom clause propositionalization with artificial neural networks

引用

MACHINE LEARNING 2014年第1期94卷 81-104页

作者： Franca, Manoel V. M. Zaverucha, Gerson Garcez, Artur S. d'Avila Univ Fed Rio de Janeiro Rio De Janeiro Brazil City Univ London London EC1V 0HB England

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. inductive logic programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-(ILP)-P-2, to perform learning from numerical vectors. C-(ILP)-P-2 uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy.

关键词： Relational learning inductive logic programming Propositionalization Neural-symbolic integration Artificial neural 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：