检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

318 篇 期刊文献
206 篇 会议
3 篇 学位论文
1 册 图书

馆藏范围

528 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

489 篇 工学
- 436 篇 计算机科学与技术...
- 96 篇 软件工程
- 60 篇 电气工程
- 47 篇 控制科学与工程
- 26 篇 信息与通信工程
- 14 篇 生物工程
- 8 篇 生物医学工程（可授...
- 5 篇 电子科学与技术（可...
- 4 篇 机械工程
- 3 篇 仪器科学与技术
- 3 篇 测绘科学与技术
- 3 篇 网络空间安全
- 2 篇 光学工程
- 2 篇 化学工程与技术
82 篇 理学
- 46 篇 数学
- 21 篇 生物学
- 5 篇 物理学
- 5 篇 统计学（可授理学、...
- 4 篇 化学
- 4 篇 系统科学
- 2 篇 地理学
38 篇 医学
- 16 篇 基础医学(可授医学...
- 12 篇 临床医学
- 4 篇 药学(可授医学、理...
- 2 篇 公共卫生与预防医...
36 篇 管理学
- 28 篇 管理科学与工程(可...
- 5 篇 图书情报与档案管...
- 4 篇 公共管理
3 篇 文学
- 2 篇 外国语言文学
3 篇 农学
- 2 篇 作物学
2 篇 哲学
1 篇 经济学
1 篇 法学
1 篇 教育学
1 篇 军事学

主题

528 篇 inductive logic ...
81 篇 machine learning
23 篇 relational learn...
23 篇 data mining
23 篇 answer set progr...
18 篇 statistical rela...
11 篇 logic programmin...
11 篇 predicate invent...
11 篇 explainable ai
9 篇 theory revision
8 篇 reinforcement le...
8 篇 semantic web
8 篇 learning
8 篇 ilp
8 篇 information extr...
7 篇 computational le...
7 篇 program synthesi...
7 篇 online learning
7 篇 first-order logi...
7 篇 heuristics

机构

9 篇 katholieke univ ...
8 篇 imperial coll lo...
7 篇 natl inst inform...
6 篇 univ london impe...
6 篇 univ bamberg cog...
6 篇 katholieke univ ...
5 篇 univ oxford oxfo...
5 篇 univ texas dalla...
5 篇 imperial coll lo...
4 篇 univ york dept c...
4 篇 univ waikato dep...
4 篇 res org informat...
4 篇 jozef stefan ins...
4 篇 univ oxford comp...
4 篇 columbia univ ct...
4 篇 tufts univ dept ...
4 篇 univ fed pernamb...
4 篇 univ new south w...
4 篇 univ toulouse 3 ...
3 篇 department of co...

作者

16 篇 inoue katsumi
13 篇 srinivasan ashwi...
13 篇 schmid ute
9 篇 costa vitor sant...
9 篇 russo alessandra
8 篇 muggleton stephe...
7 篇 cropper andrew
7 篇 blockeel h
7 篇 gupta gopal
7 篇 camacho rui
7 篇 zaverucha gerson
6 篇 dzeroski s
6 篇 ribeiro tony
6 篇 kersting kristia...
6 篇 shakerin farhad
6 篇 zelezny filip
5 篇 muggleton stephe...
5 篇 sakama chiaki
5 篇 de raedt luc
5 篇 law mark

语言

501 篇 英文
22 篇 其他
3 篇 中文
1 篇 德文
1 篇 法文

检索条件"主题词=inductive logic programming"

共 528 条记录，以下是81-90 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Phase Transition and New Fitness Function Based Genetic inductive logic programming Algorithm

Phase Transition and New Fitness Function Based Genetic Indu...

引用

IEEE Congress on Evolutionary Computation (CEC)

作者： Li, Yanjuan Guo, Maozu Harbin Inst Technol Sch Comp Sci & Technol Harbin 150006 Peoples R China

ISBN: (纸本)9781467315098

A new genetic inductive logic programming (GILP for short) algorithm named PT-NFF-GILP (Phase Transition and New Fitness Function based Genetic inductive logic programming) is proposed in this paper. Based on phase transition of the covering test, PT-NFF-GILP randomly generates initial population in phase transition region instead of the whole space of candidate clauses. Moreover, a new fitness function, which not only considers the number of examples covered by rules, but also considers the ratio of the examples covered by rules to the training examples, is defined in PT-NFF-GILP. The new fitness function measures the quality of first-order rules more precisely, and enhances the search performance of algorithm. Experiments on ten learning problems show that: 1) the new method of generating initial population can effectively reduce iteration number and enhance predictive accuracy of GILP algorithm;2) the new fitness function measures the quality of first-order rules more precisely and avoids generating over-specific hypothesis;3) The performance of PT-NFF-GILP is better than other algorithms compared with it, such as G-NET, KFOIL and NFOIL.

关键词： machine learning inductive logic programming genetic algorithm genetic inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

Application of inductive logic programming to produce emergent behavior in an artificial society 3

Application of inductive logic programming to produce emerge...

引用

3rd IIAI International Conference on Advanced Applied Informatics (IIAI-AAI)

作者： Komura, Keigo Mutoh, Atsuko Inuzuka, Nobuhiro Nagoya Inst Technol Grad Sch Engn Dept Comp Sci & Engn Nagoya Aichi Japan

ISBN: (纸本)9781479941735

Artificial society is a discipline to study mechanisms of social system and phenomena which the mechanisms make. Emergence is global phenomena occurred by local mechanisms, such as, by collective behavior of autonomous agents. Understanding of emergence phenomena is a challenging subject. In this paper we use the framework of inductive logic programming (ILP) for artificial society and emergence behavior study. ILP is a branch of machine learning based on logic programming and inductive inference. We investigate the possibility of ILP in artificial society study. ILP and logic programming technique are applied to representation of an artificial society model, called Sugarscape, and to rule learning for agent behavior. Although classical ILP algorithms target classification problems, the proposed algorithm grows behavior rule for an evaluation measurement. Phenomena which this paper treate is limited but we showed that ILP technique can be applied to study in the field of artificial society.

关键词： Artificial society Emergent behavior inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

Inferring UI Patterns with inductive logic programming

Inferring UI Patterns with Inductive Logic Programming

引用

8th Iberian Conference on Information Systems and Technologies (CISTI)

作者： Nabuco, Miguel Paiva, Ana C. R. Camacho, Rui Faria, Joao Pascoal Univ Porto Fac Engn Dept Engn Informat P-4100 Oporto Portugal

ISBN: (纸本)9789899843400

This paper presents an approach to infer UI patterns existent in a web application. This reverse engineering process is performed in two steps. First, execution traces are collected from user interactions using the Selenium software. Second, the existing UI patterns within those traces are identified using Machine Learning inference with the Aleph ILP system. The paper describes and illustrates the proposed methodology on a case study over the Amazon web site.

关键词： Reverse Engineering inductive logic programming Web Application UI Patterns

来源：评论

学校读者我要写书评

暂无评论

Application of inductive logic programming for learning ECG waveforms 6th

Application of inductive logic programming for learning ECG ...

引用

6th Conference on Artificial Intelligence in Medicine Europe (AIME 97)

作者： Kokai, G Alexin, Z Gyimothy, T ATTILA JOZSEF UNIV DEPT APPL INFORMAT H-6720 SZEGED HUNGARY HUNGARIAN ACAD SCI RES GRP ARTIFICIAL INTELLIGENCE H-6720 SZEGED HUNGARY

ISBN: (纸本)354062709X

in this paper a learning system is presented which integrates an ECG waveform classifier (called PECG) with an interactive learner (called IMPUT). The PECG system is based on an attribute grammar specification of ECGs that has been transformed to Prolog. The IMPUT system combines the interactive debugging technique IDT with the unfolding algorithm introduced in SPECTRE. Using the IMPUT system we can effectively assist in preparing the correct description of the basic structures of ECG waveforms.(4)

关键词： inductive logic programming program specialization syntactic pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Applying inductive logic programming to Self-Healing Problem in Grid Computing: Is it a feasible task?

Applying Inductive Logic Programming to Self-Healing Problem...

引用

3rd International Conference on Advanced Engineering Computing and Applications in Sciences

作者： Ferro, Mariza Mury, Antonio Roberto Schulze, Bruno Natl Lab Sci Comp ComCiDis Distributed Sci Comp Grp Petropolis RJ Brazil

ISBN: (纸本)9781424450824

Grid computing systems are extremely large and complex so, manually dealing with its failures becomes impractical. Recently, it has been proposed that the systems themselves should manage their own failures or malfunctions. This is referred as self-healing. To deal with this challenging, is required to predict and control the process through a number of automated learning and proactive actions. In this paper, we proposed inductive logic programming, a relational machine learning method, for prediction and root causal analysis that makes it possible the development of a self-healing component.

关键词： Grid Self-healing Machine learning inductive logic programming

来源：评论

学校读者我要写书评

暂无评论

Learning Strategies of inductive logic programming Using Reinforcement Learning 1

引用

32nd International Conference on inductive logic programming (ILP)

作者： Isobe, Takeru Inoue, Katsumi SOKENDAI Grad Inst Adv Studies Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (数字)9783031492990

ISBN: (纸本)9783031492983;9783031492990

Learning settings are crucial for most inductive logic programming (ILP) systems to learn efficiently. Hypothesis spaces can be huge, and ILP systems take a long time to output solutions or even cannot terminate within time limits. Therefore, users must set suitable learning settings for each ILP task to bring the best performance of the system. However, most users struggle to set appropriate settings for the task they see for the first time. In this paper, we propose a method to make an ILP system more adaptable to tasks with weak learning biases. In particular, we attempt to learn efficient strategies for an ILP system using reinforcement learning (RL). We use Popper, a state-of-the-art ILP system that implements the concept of learning from failures (LFF). We introduce RL-Popper, which divides the hypothesis space into subspaces more minutely than Popper. RL is used to learn the efficient search order of the divided spaces. We provide the details of RL-Popper and showsome empirical results.

关键词： inductive logic programming Meta Learning Learning From Failures Reinforcement Learning

来源：评论

学校读者我要写书评

暂无评论

Ontology Population from the Web: an inductive logic programming-Based Approach

Ontology Population from the Web: an Inductive Logic Program...

引用

11th International Conference on Information Technology - New Generations (ITNG)

作者： Lima, Rinaldo Oliveira, Hilario Freitas, Fred Espinasse, Bernard Univ Fed Pernambuco Informat Ctr Recife PE Brazil Aix Marseille Univ LSIS Marseille France

ISBN: (纸本)9781479931873

The rapid growth of the Web and the information overload problem demand the development of practical information extraction (IE) solutions for web content processing. Ontology Population (OP) concerns both the extraction and classification of instances of the concepts and relations defined by an ontology. Developing IE rules for OP is an intensive and time-consuming process. Thus, an automated mechanism, based on machine-learning techniques, able to convert textual data from web pages into ontology instances may be a crucial path. This paper presents an inductive logic programming-based method that automatic induces symbolic extraction rules, which are used for populating a domain ontology with instances of entity classes. This method uses domain-independent linguistic patterns for retrieving candidate instances from web pages, and a WordNet semantic similarity measure as background knowledge to be used as input by a generic inductive logic programming system. Experiments were conducted concerning both the instance classification problem and a comparison with other popular machine learning algorithms, with encouraging results.

关键词： Ontology Population inductive logic programming Pattern Learning Information Extraction

来源：评论

学校读者我要写书评

暂无评论

Qualitative knowledge driven approach to inductive logic programming

Qualitative knowledge driven approach to inductive logic pro...

引用

International Conference on Industrial and Information Systems

作者： Ranasinghe, D. D. M. Karunananda, A. S. Open Univ Sri Lanka Dept Math & Comp Sci Fac Nat Sci Nugegoda Sri Lanka Univ Moratuwa Fac Informat Technol Moratuwa Sri Lanka

ISBN: (纸本)9781424403219

Automating the learning of context specific robust rules from an evolving scene has been a research challenge in the area of cognitive vision systems. A research has been conducted to develop a system that learns context specific rules from an evolving scene by abstracting a model of human visual learning. Our system treats a set of symbolic data generated from an evolving real world scene and background knowledge as input to the system and inductive logic programming techniques are used to learn rules of the scene. The observed visual scene is represented in terms of qualitative spatial and temporal relations and these learnt relations are considered as input examples for inductive learning. A prototype has been developed for learning from various scenes of setting dinner tables. Currently the system is being tested to incorporate learning from different real world scenes thus improving the generalization power as well as combine more spatial and temporal representation and reasoning mechanisms to further enhance human like learning. This work can be adopted in automating a robot learning of object manipulation based on a visual scene.

关键词： cognitive systems inductive logic programming learning by example cognitive vision systems dinner table setting human visual learning qualitative knowledge robot learning visual scene

来源：评论

学校读者我要写书评

暂无评论

Efficient inductive logic programming Based on Particle Swarm Optimization 4th

Efficient Inductive Logic Programming Based on Particle Swar...

引用

4th European-Alliance-for-Innovation (EAI) International Conference on Artificial Intelligence for Communications and Networks (AICON)

作者： Obara, Kyosuke Takimoto, Munehiro Kumazawa, Tsutomu Kambayashi, Yasushi Tokyo Univ Sci Dept Informat Sci Chiba Japan Software Res Associates Inc Toshima Ku Tokyo Japan Nippon Inst Technol Dept Comp & Informat Engn Saitama Japan

ISBN: (纸本)9783031291258;9783031291265

inductive logic programming (ILP) is an inductive reasoning method based on the first-order predicative logic. This technology is widely used for data mining using symbolic artificial intelligence. ILP searches for a suitable hypothesis that covers positive examples and uncovers negative examples. The searching process requires a lot of execution cost to interpret many given examples for practical problems. In this paper, we propose a new hypothesis search method using particle swarm optimization (PSO). PSO is a meta-heuristic algorithm based on behaviors of particles. In our approach, each particle repeatedly moves from a hypothesis to another hypothesis within a hypothesis space. At that time, some hypotheses are refined based on the value returned by a predefined evaluation function. Since PSO just searches a part of the hypothesis space, it contributes to the speed up of the execution of ILP. In order to demonstrate the effectiveness of our method, we have implemented it on Progol that is one of the ILP systems [6], and then we conducted numerical experiments. The results showed that our method reduced the hypothesis search time compared to another conventional Progol.

关键词： inductive logic programming Particle swarm optimization Progol

来源：评论

学校读者我要写书评

暂无评论

Differentiable neural logic networks and their application onto inductive logic programming

Differentiable neural logic networks and their application o...

引用

作者： Payani, Ali Georgia Institute of Technology

学位级别：博士

Despite the impressive performance of Deep Neural Networks (DNNs), they usually lack the explanatory power of disciplines such as logic programming. Even though they can learn to solve very difficult problems, the learning is usually implicit and it is very difficult, if not impossible, to interpret and decipher the underlying explanations that is implicitly stored in the weights of the neural network models. On the other hand, standard logic programming is usually limited in scope and application compared to the DNNs. The objective of this dissertation is to bridge the gap between these two disciplines by presenting a novel paradigm for learning algorithmic and discrete tasks via neural networks. This novel approach, uses the differentiable neural network to design interpretable and explanatory models that can learn and represent Boolean functions efficiently. We will investigate the application of these differentiable Neural logic (dNL) networks in disciplines such as inductive logic programming, Relational Reinforcement Learning, as well as in discrete algorithmic tasks such as decoding LDPC codes over Binary erasure Channels. inductive logic programming (ILP) is an important branch of machine learning that uses formal logic to define and solve problems. Compared to the DNNs, ILP provides high degree of interpretability and can learn from small number of examples and has superior generalization ability. However, standard ILP solvers are not differentiable and cannot benefit from the impressive power of DNNs. In this dissertation, we reformulate the ILP as a differentiable neural network by exploiting the explanatory power of dNL networks. This novel neural based ILP solver (dNL-ILP) is capable of learning auxiliary and invented predicates as well as learning complex recursive predicates. We show that dNL-ILP outperforms the current state of the art ILP solvers in a variety of benchmark algorithmic tasks as well as larger scale relational classification tasks. In

关键词： inductive logic programming ILP Neural logic Relational reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共53页 << < 5 6 7 8 9 10 11 12 13 14 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：