没有合适的资源?快使用搜索试试~ 我知道了~
首页Machine learning: Trends,perspectives, and prospects
Machine learning: Trends,perspectives, and prospects
5星 · 超过95%的资源 需积分: 13 93 下载量 157 浏览量
更新于2023-03-16
评论 1
收藏 1.43MB PDF 举报
M. I. Jordan和T. M. Mitchell在Science上发表了一篇论文Machine learning: Trends,perspectives, and prospects,这篇论文是7月16日发布的,还挺新的,对机器学习的基本概念,发展状况,应用,常见的学习算法做了介绍。对于刚入门来说,构建一个比较基本的机器学习的概念图。 好东西就要拿出来分享,要资源分就不好了
资源详情
资源评论
资源推荐
Despite practical challe nges, we are hopeful that
informeddiscussionsamongpolicy-makersandthe
public about data and the capabilities of machine
learning, will lead to insightful designs of programs
and policies that can balance the goals of protecting
privacy and ensuring fairness with those of reaping
the benefits to scientific research and to individual
and public health. Our commitments to privacy and
fairness are evergreen, but our policy choices must
adapt to advance them, and support new tech-
niques for deepening our knowledge.
REFERENCES AND NOTES
1. M. De Choudhury, S. Counts, E. Horvitz, A. Hoff, in Proceedings
of International Conference on Weblogs and Social Media
[Association for the Advancement of Artificial Intelligence
(AAAI), Palo Alto, CA, 2014].
2. J. S. Brownstein, C. C. Freifeld, L. C. Madoff, N. Engl. J. Med.
360, 2153–2155 (2009).
3. G. Eysenbach, J. Med. Internet Res. 11, e11 (2009).
4. D. A. Broniatowski, M. J. Paul, M. Dredze, PLOS ONE 8, e83672
(2013).
5. A. Sadilek, H. Kautz, V. Silenzio, in Proceedings of the
Twenty-Sixth AAAI Conference on Artificial Intelligence
(AAAI, Palo Alto, CA, 2012).
6. M. De Choudhury, S. Counts, E. Horvitz, in Proceedings of the
SIGCHI Conference on Human Factors in Computing Systems
(Association for Computing Machinery, New York, 2013),
pp. 3267–3276.
7. R. W. White, R. Harpaz, N. H. Shah, W. DuMouchel, E. Horvitz,
Clin. Pharmacol. Ther. 96, 239–246 (2014).
8. Samaritans Radar; www.samaritans.org/how-we-can-help-you/
supporting-someone-online/samaritans-radar.
9. Shut down Samaritans Radar; http://bit.ly/Samaritans-after.
10. U.S. Equal Employment Opportunity Commission (EEOC), 29
Code of Federal Regulations (C.F.R.), 1630.2 (g) (2013).
11. EEOC, 29 CFR 1635.3 (c) (2013).
12. M. A. Rothstein, J. Law Med. Ethics 36, 837–840 (2008).
13. Executive Office of the President, Big Data: Seizing
Opportunities, Preserving Values (White House, Washington,
DC, 2014); http://1.usa.gov/1TSOhiG.
14. Letter from Maneesha Mithal, FTC, to Reed Freeman, Morrison,
& Foerster LLP, Counsel for Netflix, 2 [closing letter] (2010);
http://1.usa.gov/1GCFyXR.
15. In re Facebook, Complaint, FTC File No. 092 3184 (2012).
16. FTC Staff Report, Mobile Privacy Disclosures: Building Trust Through
Transparency (FTC,Washington,DC,2013);http://1.usa.gov/1eNz8zr.
17. FTC, Protecting Consumer Privacy in an Era of Rapid Change:
Recommendations for Businesses and Policymakers (FTC,
Washington, DC, 2012).
18. Directive 95/46/ec of the European Parliament and of The
Council of Europe, 24 October 1995.
19. L. Sweeney, Online ads roll the dice [blog]; http://1.usa.gov/
1KgEcYg.
20. FTC, “Big data: A tool for inclusion or exclusion?” (workshop,
FTC, Washington, DC, 2014); http://1.usa.gov/1SR65cv
21. FTC, Data Brokers: A Call for Transparency and Accountability
(FTC, Washington, DC, 2014); http://1.usa.gov/1GCFoj5.
22. J. Podesta, “Big data and privacy: 1 year out” [blog]; http://bit.
ly/WHsePrivacy.
23. White House Council of Economic Advisers, Big Data and
Differential Pricing (White House, Washington, DC, 2015).
24. Executive Office of the President, Big Data and Differential Processing
(White House, Washington, DC, 2015); http://1.usa.gov/1eNy7qR.
25. Executive Office of the President, Big Data: Seizing
Opportunities, Preserving Values (White House, Washington,
DC, 2014); http://1.usa.gov/1TSOhiG.
26. President’s Council of Advisors on Science and Technology
(PCAST), Big Data and Privacy: A Technological Perspective
(White House, Washington, DC, 2014); http://1.usa.gov/1C5ewNv.
27. European Commission, Proposal for a Regulation of the European
Parliament and of the Council on the Protection of Individuals
with regard to the processing of personal data and on the free
movement of such data (General Data Protection Regulation),
COM(2012) 11 final (2012); http://bit.ly/1Lu5POv.
28. M. Schrems v. Facebook Ireland Limited, §J. Unlawful data
transmission to the U.S.A. (“PRISM”), ¶166 and 167 (2013);
www.europe-v-facebook.org/sk/sk_en.pdf.
10.1126/science.aac4520
REVIEW
Machine learning: Trends,
persp ectives, and prospects
M. I. Jordan
1
* and T. M. Mitchell
2
*
Machine learning addresses the question of how to build computers that improve
automatically through experience. It is one of today’s most rapidly growing technical fields,
lying at the intersection of computer science and statistics, and at the core of artificial
intelligence and data science. Recent progress in machine learning has been driven both by
the development of new learning algorithms and theory and by the ongoing explosion in the
availability of online data and low-cost computation. The adoption of data-intensive
machine-learning methods can be found throughout science, technology and commerce,
leading to more evidence-based decision-making across many walks of life, including
health care, manufacturing, education, financial modeling, policing, and marketing.
M
achine learning is a discipline focused
on two interrelated questions: How can
one construct computer systems that auto-
matically improve through experience?
and What are the fundamental statistical-
computational-information-theoretic laws that
govern all learning systems, including computers,
humans, and organizations? The study of machine
learning is important both for addressing these
fundamental scientific and engineering ques-
tions and for the highly practical computer soft-
wareithasproducedandfieldedacrossmany
applications.
Machine learning has progressed dramati-
cally over the past two decades, from laboratory
curiosity to a practical technology in widespread
commercial use. Within artificial intelligence (AI),
machine learning has emerged as the method
of choice for developing practical software for
computer vision, speech recognition, natural lan-
gu a ge processing, robot control, and other ap-
plications. Many developers of AI systems now
recognize that, for many applications, it can be
far easier to train a system by showing it exam-
ples of desired input-output behavior than to
program it manually by anticipating the desired
response for all possible inputs. The effect of ma-
chine learning has also been felt broadly across
co mputer science and across a range of indus-
tries concerned with data-intensive issues, such
as consumer services, the diagnosis of faults in
complex systems, and the control of logistics
chains. There has been a similarly broad range of
effects across empirical sciences, from biology to
cosmology to social science, as machine-learning
methods have been developed to analyze high-
throughput experimental data in novel ways. See
Fig. 1 for a depiction of some recent areas of ap-
plication of machine learning.
A learning problem can be defined as the
problem of improving some measure of perform-
an c e when executing some task, through some
type of training experience. For example, in learn-
ing to detect credit-card fraud, the task is to as-
sign a label of “fraud” or “not fraud” to any given
credit-card transaction. The performance metric
to be improved might be the accuracy of this
fraud classifier , and the training experience might
consist of a collection of historical credit-card
transactions, each labeled in retrospect as fraud-
ulent or not. Alternatively, one might define a
different performance metric that assigns a higher
penalty when “fraud” is labeled “not fraud” than
when “not fraud” is incorrectly labeled “fraud.”
One mig ht also def in e a di f ferent type of training
experience—for example, by including unlab-
eled credit-card transactions along with labeled
examples.
A diverse array of machine-learning algorithms
ha s been developed to cover the wide variety of
data and problem types exhibited across differ-
ent machine-learning problems (1, 2). Conceptual-
ly, machine-learning algorithms can be viewed as
searching through a large space of candidate
programs, guided by training experience, to find
a program that optimizes the performance metric.
Machine-learning algorithms vary greatly, in part
by the way in which they represent candidate
programs (e.g., decision trees, mathematical func-
tions, and general programming languages) and in
partbythewayinwhichtheysearchthroughthis
space of programs (e.g., optimization algorithm s
with well-understood convergence guarantees
and evolutionary search methods that evaluate
successivegenerationsofrandomlymutatedpro-
gr a ms). Here, we focus on approaches that have
been particularly successful to date.
Many algorithms focus on function approxi-
mation problems, where the task is embodied
in a function (e.g., given an input transaction, out-
put a “fraud” or “not fraud” label), and the learn-
ing problem is to improve the accuracy of that
function, with experience consisting of a sample
of known input-output pai rs of the fun ction . In
some cases, the function is represented explicit-
ly as a parameterized functional form; in other
cases, the function is implicit and obtained via a
search process, a factorization, an optimization
SCIENCE sciencemag.org 17 JULY 20 15 • VOL 349 ISSUE 6245 255
1
Department of Electrical Engineering and Computer
Sciences, Department of Statistics, University of California,
Berkeley, CA, USA.
2
Machine Learning Department, Carnegie
Mellon University, Pittsburgh, PA, USA.
*Corresponding author. E-mail: jordan@cs.berkeley.edu (M.I.J.);
tom.mitchell@cs.cmu.edu (T.M.M.)
on July 16, 2015www.sciencemag.orgDownloaded from on July 16, 2015www.sciencemag.orgDownloaded from on July 16, 2015www.sciencemag.orgDownloaded from on July 16, 2015www.sciencemag.orgDownloaded from on July 16, 2015www.sciencemag.orgDownloaded from on July 16, 2015www.sciencemag.orgDownloaded from
因式分解
猪杂汤饭
- 粉丝: 233
- 资源: 1
上传资源 快速赚钱
- 我的内容管理 收起
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
会员权益专享
最新资源
- RTL8188FU-Linux-v5.7.4.2-36687.20200602.tar(20765).gz
- c++校园超市商品信息管理系统课程设计说明书(含源代码) (2).pdf
- 建筑供配电系统相关课件.pptx
- 企业管理规章制度及管理模式.doc
- vb打开摄像头.doc
- 云计算-可信计算中认证协议改进方案.pdf
- [详细完整版]单片机编程4.ppt
- c语言常用算法.pdf
- c++经典程序代码大全.pdf
- 单片机数字时钟资料.doc
- 11项目管理前沿1.0.pptx
- 基于ssm的“魅力”繁峙宣传网站的设计与实现论文.doc
- 智慧交通综合解决方案.pptx
- 建筑防潮设计-PowerPointPresentati.pptx
- SPC统计过程控制程序.pptx
- SPC统计方法基础知识.pptx
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功
评论3