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

95 册 图书

馆藏范围

95 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

42 篇 工学
- 36 篇 计算机科学与技术...
- 27 篇 控制科学与工程
- 25 篇 软件工程
- 5 篇 机械工程
- 5 篇 仪器科学与技术
- 2 篇 信息与通信工程
- 1 篇 电子科学与技术（可...
27 篇 管理学
- 27 篇 管理科学与工程(可...
- 2 篇 工商管理
13 篇 理学
- 7 篇 系统科学
- 6 篇 数学
- 2 篇 统计学（可授理学、...
4 篇 经济学
- 4 篇 应用经济学

主题

56 篇 artificial intel...
51 篇 machine learning
46 篇 mathematical mod...
6 篇 computer science
3 篇 computers
2 篇 artificial intel...
2 篇 artificial intel...
2 篇 reasoning
2 篇 supervised learn...
2 篇 the internet
2 篇 decision making
2 篇 computer \u0026 ...
2 篇 amp
2 篇 computer vision
2 篇 robotics
1 篇 programming lang...
1 篇 bisac category: ...
1 篇 reinforcement le...
1 篇 support vector m...
1 篇 models

机构

10 篇 nokia bell labs
7 篇 stanford univers...
4 篇 ibm research
4 篇 carnegie mellon ...
3 篇 university of ca...
3 篇 the university o...
3 篇 rwth aachen univ...
3 篇 unsw sydney
3 篇 bar-ilan univers...
2 篇 university of te...
2 篇 technical univer...
2 篇 university of ox...
2 篇 tsinghua univers...
2 篇 nasa ames resear...
2 篇 university of gi...
2 篇 georgia institut...
2 篇 university of me...
2 篇 jerusalem colleg...
2 篇 nanyang technolo...
2 篇 university of po...

作者

4 篇 zhiyuan liu
4 篇 michael genesere...
2 篇 tianjian chen
2 篇 qiang yang
2 篇 michael thielsch...
2 篇 rina dechter
2 篇 mausam
2 篇 han yu
2 篇 vinay k. chaudhr...
2 篇 maosong sun
2 篇 yan kang
2 篇 cunchao tu
2 篇 william l. hamil...
2 篇 murat kantarciog...
2 篇 kristen grauman
2 篇 colin campbell
2 篇 yiming ying
2 篇 lirong xia
2 篇 bastian leibe
2 篇 cheng yang

语言

89 篇 英文
6 篇 中文

检索条件"丛书名=Synthesis Lectures on Artificial Intelligence & Machine Learning"

共 95 条记录，以下是41-50 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：

Markov Logic: An Interface Layer for artificial intelligence 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

2009年

作者： Domingos, Pedro Lowd, Daniel

ISBN: (数字)9781598296938

ISBN: (纸本)9781598296921

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system.

关键词： artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Introduction to Semi-Supervised learning

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

2009年

作者： Zhu, Xiaojin Goldberg, Andrew

ISBN: (数字)9781598295481

ISBN: (纸本)9781598295474

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical machine learning / Overview of Semi-Supervised learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised learning / Semi-Supervised Support Vector machines

关键词： Supervised learning (machine learning) machine learning

来源：评论

学校读者我要写书评

暂无评论

Representation Discovery using Harmonic Analysis 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

2008年

作者： Mahadevan, Sridhar

ISBN: (数字)9781598296600

ISBN: (纸本)9781598296594

Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research. Tab

关键词： Computers amp the Internet artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Action Programming Languages

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

2008年

作者： Thielscher, Michael

ISBN: (数字)9781598295450

ISBN: (纸本)9781598295443

artificial systems that think and behave intelligently are one of the most exciting and challenging goals of artificial intelligence. Action Programming is the art and science of devising high-level control strategies for autonomous systems which employ a mental model of their environment and which reason about their actions as a means to achieve their goals. Applications of this programming paradigm include autonomous software agents, mobile robots with high-level reasoning capabilities, and General Game Playing. These lecture notes give an in-depth introduction to the current state-of-the-art in action programming. The main topics are knowledge representation for actions, procedural action programming, planning, agent logic programs, and reactive, behavior-based agents. The only prerequisite for understanding the material in these lecture notes is some general programming experience and basic knowledge of classical first-order logic. Table of Contents: Introduction / Mathematical Preliminaries / Procedural Action Programs / Action Programs and Planning / Declarative Action Programs / Reactive Action Programs / Suggested Further Reading

关键词： Programming Languages (Electronic Computers)

来源：评论

学校读者我要写书评

暂无评论

Essentials of Game Theory: A Concise Multidisciplinary Introduction 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

2008年

作者： Leyton-Brown, Kevin Shoham, Yoav

ISBN: (数字)9781598295948

ISBN: (纸本)9781598295931

Game theory is the mathematical study of interaction among independent, self-interested agents. The audience for game theory has grown dramatically in recent years, and now spans disciplines as diverse as political science, biology, psychology, economics, linguistics, sociology, and computer science, among others. What has been missing is a relatively short introduction to the field covering the common basis that anyone with a professional interest in game theory is likely to require. Such a text would minimize notation, ruthlessly focus on essentials, and yet not sacrifice rigor. This synthesis Lecture aims to fill this gap by providing a concise and accessible introduction to the field. It covers the main classes of games, their representations, and the main concepts used to analyze them.

关键词： Computers amp the Internet artificial intelligence

来源：评论

学校读者我要写书评

暂无评论

Intelligent Autonomous Robotics: A Robot Soccer Case Study 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

2007年

作者： Stone, Peter

ISBN: (数字)9781598291278

ISBN: (纸本)9781598291261

Robotics technology has recently advanced to the point of being widely accessible for relatively low-budget research, as well as for graduate, undergraduate, and even secondary and primary school education. This lecture provides an example of how to productively use a cutting-edge advanced robotics platform for education and research by providing a detailed case study with the Sony AIBO robot, a vision-based legged robot. The case study used for this lecture is the UT Austin Villa RoboCup Four-Legged Team. This lecture describes both the development process and the technical details of its end result. The main contributions of this lecture are (i) a roadmap for new classes and research groups interested in intelligent autonomous robotics who are starting from scratch with a new robot, and (ii) documentation of the algorithms behind our own approach on the AIBOs with the goal of making them accessible for use on other vision-based and/or legged robot platforms.

关键词： Technology Engineering

来源：评论

学校读者我要写书评

暂无评论

A Concise Introduction to Multiagent Systems and Distributed artificial intelligence 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

2007年

作者： Vlassis, Nikos

ISBN: (数字)9781598295276

ISBN: (纸本)9781598295269

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.

关键词：

来源：评论

学校读者我要写书评

暂无评论

learning with Support Vector machines 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Colin Campbell Yiming Ying

ISBN: (数字)9783031015526

ISBN: (纸本)9783031004247

Support Vectors machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data. Table of Contents: Support Vector machines for Classification / Kernel-based Models / learning with Kernels

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

来源：评论

学校读者我要写书评

暂无评论

Visual Object Recognition 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Kristen Grauman Bastian Leibe

ISBN: (数字)9783031015533

ISBN: (纸本)9783031004254

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

来源：评论

学校读者我要写书评

暂无评论

Computational Aspects of Cooperative Game Theory 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Georgios Chalkiadakis Edith Elkind Michael Wooldridge

ISBN: (数字)9783031015588

ISBN: (纸本)9783031004308

Cooperative game theory is a branch of (micro-)economics that studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible. Our aim in this book is to present a survey of work on the computational aspects of cooperative game theory. We begin by formally defining transferable utility games in characteristic function form, and introducing key solution concepts such as the core and the Shapley value. We then discuss two major issues that arise when considering such games from a computational perspective: identifying compact representations for games, and the closely related problem of efficiently computing solution concepts for games. We survey several formalisms for cooperative games that have been proposed in the literature, including, for example, cooperative games defined on networks, as well as general compact representation schemes such as MC-nets and skill games. As a detailed case study, we consider weighted voting games:a widely-used and practically important class of cooperative games that inherently have a natural compact representation. We investigate the complexity of solution concepts for such games, and generalizations of them. We briefly discuss games with non-transferable utility and partition function games. We then overview algorithms for identifying welfare-maximizing coalition structures and methods used by rational agents to form coalitions (even under uncertainty), including bargaining algorithms. We conclude by considering some developing topics, applications, and future research directions.

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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10页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：