'Machine Learning' 카테고리의 글 목록 (2 Page)

'Machine Learning'에 해당되는 글 16건

GRE CS subject

2009. 3. 4. 15:00

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Journal Ranking

Machine Learning 2009. 3. 4. 13:55

No.	Journal	Rank	JII	PII	B2	B4	B6
1	MACHINE LEARNING	379	13.14	295.21	5.54	8.06	8.45
2	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE ...	441	42.15	267.19	6.71	8.82	9.47
3	INTERNATIONAL JOURNAL OF COMPUTER VISION	644	11.74	200.63	6.27	6.68	7.20
4	ARTIFICIAL INTELLIGENCE	694	13.29	188.45	4.86	5.88	6.32
5	NEURAL COMPUTATION	728	19.99	181.28	4.95	5.44	5.63
6	NETWORK-COMPUTATION IN NEURAL SYSTEMS	964	3.41	143.49	3.82	4.61	3.83
7	IEEE TRANSACTIONS ON IMAGE PROCESSING	1124	17.79	125.31	4.32	6.68	7.54
8	JOURNAL OF MACHINE LEARNING RESEARCH	1225	4.19	114.22	10.18	10.35	7.11
9	NEURAL NETWORKS	1398	11.03	100.93	3.03	4.28	4.55
10	EVOLUTIONARY COMPUTATION	1445	1.71	96.88	3.12	5.20	8.92
11	IEEE TRANSACTIONS ON NEURAL NETWORKS	1510	13.08	91.77	4.15	5.06	5.51
12	COMPUTER SPEECH AND LANGUAGE	1570	1.87	86.88	0.92	1.59	1.67
13	MEDICAL IMAGE ANALYSIS	1714	2.84	78.98	5.55	7.28	6.32
14	CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS	1723	7.03	78.54	3.39	4.35	4.30
15	DATA MINING AND KNOWLEDGE DISCOVERY	1728	1.45	78.45	3.74	5.42	4.77
16	JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH	1830	2.55	73.46	4.10	4.45	4.57
17	COMPUTER VISION AND IMAGE UNDERSTANDING	1949	3.71	68.05	2.59	3.59	4.50
18	PATTERN RECOGNITION	2064	14.59	63.94	3.96	3.88	3.53
19	IEEE TRANSACTIONS ON FUZZY SYSTEMS	2122	4.38	61.73	3.22	4.42	5.44
20	COGNITIVE BRAIN RESEARCH	2236	9.02	57.64	4.58	5.04	4.43
21	IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION	2731	1.81	44.05	5.99	7.47	7.35
22	ARTIFICIAL LIFE	2779	0.94	42.91	3.29	3.98	4.38
23	IEEE INTELLIGENT SYSTEMS	2838	2.02	41.48	4.69	6.01	5.75
24	CONNECTION SCIENCE	2888	0.55	40.50	2.08	1.80	2.03
25	KNOWLEDGE ENGINEERING REVIEW	2905	0.40	40.26	9.67	5.38	4.82
26	COMPUTATIONAL INTELLIGENCE	2953	0.91	39.51	0.87	2.33	2.26
27	JOURNAL OF AUTOMATED REASONING	3121	0.61	36.37	2.59	2.20	2.53
28	AI MAGAZINE	3138	0.89	36.15	3.35	3.32	3.28
29	IMAGE AND VISION COMPUTING	3196	3.40	35.14	2.39	2.55	2.60
30	JOURNAL OF MATHEMATICAL IMAGING AND VISION	3333	1.11	32.78	3.93	3.11	2.92
31	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERIN...	3574	3.41	29.31	3.28	2.86	2.75
32	MACHINE VISION AND APPLICATIONS	3878	0.73	25.24	1.06	1.21	1.41
33	INTERNATIONAL JOURNAL OF APPROXIMATE REASONING	4073	0.85	22.67	1.70	1.71	1.78
34	JOURNAL OF INTELLIGENT INFORMATION SYSTEMS	4152	0.49	21.52	2.19	2.35	2.00
35	PATTERN RECOGNITION LETTERS	4169	4.65	21.36	2.09	1.93	1.67
36	AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS	4228	0.40	20.72	5.03	4.78	4.81
37	IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS P...	4272	2.65	20.15	1.97	2.23	2.32
38	JOURNAL OF HEURISTICS	4299	0.49	19.77	1.07	1.86	2.02
39	DECISION SUPPORT SYSTEMS	4333	1.43	19.36	1.63	2.49	2.63
40	ARTIFICIAL INTELLIGENCE REVIEW	4475	0.49	17.81	1.43	1.93	1.86
41	ARTIFICIAL INTELLIGENCE IN MEDICINE	4511	0.82	17.36	3.13	3.12	3.01
42	ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE	4715	0.69	15.31	0.96	1.66	1.44
43	INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND A...	4734	1.01	15.13	1.15	1.33	1.23
44	NEUROCOMPUTING	4833	3.72	13.97	1.48	1.39	1.18
45	JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL I...	4851	0.28	13.81	0.73	1.14	1.25
46	PATTERN ANALYSIS AND APPLICATIONS	4868	0.39	13.64	1.59	1.98	1.87
47	IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS P...	4927	0.52	13.11	1.16	1.95	1.99
48	AUTONOMOUS ROBOTS	4942	0.43	13.01	2.25	2.84	2.69
49	JOURNAL OF INTELLIGENT & FUZZY SYSTEMS	5041	0.17	12.31	0.53	1.47	1.91
50	AI COMMUNICATIONS	5064	0.28	12.12	1.10	1.35	1.25
51	NEURAL PROCESSING LETTERS	5099	0.46	11.88	1.31	1.17	1.39
52	CONSTRAINTS	5107	0.20	11.84
53	COMPUTING AND INFORMATICS	5143	0.14	11.60	0.50	1.34	0.95
54	MINDS AND MACHINES	5240	0.24	10.84	0.35	0.51	0.47
55	ROBOTICS AND AUTONOMOUS SYSTEMS	5261	0.77	10.67	1.29	1.27	1.33
56	DATA & KNOWLEDGE ENGINEERING	5301	0.54	10.43	2.00	2.14	1.86
57	INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS	5598	0.55	8.40	1.18	1.34	1.25
58	MECHATRONICS	5662	0.57	8.04	1.14	1.09	1.10
59	APPLIED ARTIFICIAL INTELLIGENCE	5698	0.33	7.84	1.10	1.16	1.40
60	INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND...	5766	0.38	7.44	0.83	1.19	1.02
61	EXPERT SYSTEMS	5803	0.16	7.21	1.09	1.08	1.02
62	REAL-TIME IMAGING	5915	0.24	6.41	1.19	1.15	1.06
63	NEURAL COMPUTING & APPLICATIONS	6008	0.18	5.81	0.77	0.81	0.91
64	AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DE...	6059	0.08	5.52	0.43	1.06	1.04
65	APPLIED INTELLIGENCE	6079	0.19	5.38	0.94	1.30	1.33
66	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENC...	6239	0.28	4.49	1.14	1.38	1.35
67	KNOWLEDGE-BASED SYSTEMS	6246	0.16	4.47	1.22	1.19	1.29
68	JOURNAL OF INTELLIGENT MANUFACTURING	6394	0.18	3.74	0.63	0.96	1.10
69	EXPERT SYSTEMS WITH APPLICATIONS	6437	0.37	3.50	2.32	2.33	1.91
70	SOFT COMPUTING	6530	0.24	3.11	1.02	0.91	0.85
71	JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS	6539	0.16	3.05	0.39	0.50	0.51
72	ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENG...	6556	0.08	2.97	0.08	0.20	0.22
73	INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND ...	6592	0.11	2.85	0.98	1.58	1.21
74	LECTURE NOTES IN ARTIFICIAL INTELLIGENCE	6679	6.73	2.43	0.71	0.61	0.53
75	INTELLIGENT AUTOMATION AND SOFT COMPUTING	6729	0.05	2.18	0.26	0.24	0.38
76	INTEGRATED COMPUTER-AIDED ENGINEERING	6743	0.06	2.13	0.69	0.63	0.60
77	ADVANCED ENGINEERING INFORMATICS	6866	0.02	1.58	0.81	1.16	0.77
78	JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATI...	7098	0.06	0.56	0.21	0.19	0.15
79	ARTIFICIAL INTELLIGENCE IN ENGINEERING	7389	N/A	N/A
80	COMPUTERS AND ARTIFICIAL INTELLIGENCE	7390	N/A	N/A
81	AUTOMATIC CONTROL AND COMPUTER SCIENCES	7860	N/A	N/A	0.00	0.00	0.00
82	ALL_JOURNALS	7886	N/A	N/A

Journal Impact Factors

Machine Learning 2009. 3. 4. 13:47

Miscellaneous » Journal Impact Factors

I have started monitoring the Robotics and Machine Learning journals. Based on this list of ISI impact factors for 2006 and based on ISIKNOWLEDGE.COM for 2007, I have created a list of the most current impact factors. As impact factors for 2008 are measured on 2008 publications, the impact factors for 2008 will not be available until mid-2009. My best tools right now are ISI-KNOWLEDGE and SCImago Rank. NL mean "Not Listed".

Robotics Journals	ISI Impact Factor 2006	ISI Impact Factor 2007	ISI Half- Life 2007	H-Index 2007	SCImago SJR 2007
IEEE Transactions on Robotics	1.763	1.976	9.5	15	0.067
International Journal of Robotics Research	1.591	1.318	8.5	42	0.084
Autonomous Robots	1.578	1.413	5.9	36	0.091
International Journal of Medical Robotics and Computer Assisted	0.917	0.700	NL	6	0,064
Robotics & Autonomous Systems	0.832	0.633	6.1	33	0,094
Robotics and Computer-Integrated Manufacturing	0.810	0.804	6.0	24	0,056
IEEE Robotics & Automation Magazine	0.652	0.892	5.9	22	0,081
Robotica	0.483	0.410	6.7	21	0,050
International Journal of Robotics & Automation	0.404	0.203	NL	8	0,043
Advanced Robotics	0.318	0.504	5.0	17	0,051
Industrial Robot	0.278	NL	NL	NL	NL
Journal of Intelligent & Robotic Systems	0.265	0.459	6.0	NL	NL
Journal of Robotic Systems	0.402	0.483	9.4	NL	NL

Note that the IEEE Transactions on Robotics was originally much lower (only 0.813 in 2007). The editor-in-chief Alessandro De Luca achieved a recount which lifted the journal from #5 in Robotics to #1. Subsequently, Thomson Reuters, the official publisher of JCR and ISI Web of Knowledge/Science, corrected their results based on their own data. Upon request, I can provide the printouts of the ISI rankings, but the results can also be obtained from Thomson Reuters upon subscription. I cannot make these printouts publicly available due to legal reasons. Also note that the SCImago needs to be interpreted with a grain of salt as it is not based on the updated JCR results.

Similarly, I have looked at Machine Learning journals including modern journals as well as old-fashioned ones.

Machine Learning Journals	Impact Factor 2006	Impact Factor 2007	Half- Life	H-Index 2007	SCImago SJR 2007
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE	4.306	3.579	9.7	111	0,128
MACHINE LEARNING	2.654	1.742	>10.0	54	0,128
IEEE TRANSACTIONS ON NEURAL NETWORKS	2.620	2.769	8.1	70	0,108
JOURNAL OF MACHINE LEARNING RESEARCH	2.255	2.682	4.3	31	0,127
NEURAL COMPUTATION	2.229	2.335	8.4	67	0,257
NEURAL NETWORKS	2.000	1.951	8.5	54	0,249
PATTERN RECOGNITION	1.822	2.019	7.5	57	0,078
PATTERN RECOGNITION LETTERS	0.952	0.853	6.0	39	0,065
NEUROCOMPUTING	0.860	0.865	4.8	32	0,076
NEURAL PROCESSING LETTERS	0.753	0.580	7.0	16	0,059
NEURAL COMPUTING & APPLICATIONS	0.610	0.627	5.5	13	0,062
PATTERN ANALYSIS AND APPLICATIONS	0.400	0.515	5.5	16	0,048

Apologies for the upper cases but cut & paste is soo much easier...

While any quantitative measure on scientific quality is at best dubious, the recent ranking on robotics journal rankings seems to confirm the listing above.

ML 스터디그룹

2009. 2. 26. 00:04

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Machine Learning Course at Stanford

Machine Learning 2009. 2. 20. 14:37

Stnf

CS 229
Machine Learning
Course Materials

Handouts

Handout #1: Course Information (HTML)
Handout #2: Tentative Course Schedule (HTML)
Handout #3: Project Guidelines (ps) (pdf) (corrected due times, 10/16)
Handout #4: Problem Set 1 (ps) (pdf) (data)
Handout #5: Problem Set 2 (ps) (pdf) (starter code)
Handout #6: The Simplified SMO Algorithm (ps) (pdf) (corrected error: 10/22)

Reference article: J. Platt, Fast Training of Support Vector Machines using Sequential Minimal Optimization (ps.gz) (pdf)

Handout #7: Problem Set 1 Solutions (ps) (pdf)
Handout #8: Problem Set 3 (ps) (pdf) (images)
Handout #9: Anonymous midterm survey (ps) (pdf)
Handout #10: Practice Midterm (ps) (pdf)
Handout #11: Practice Midterm Solutions (ps) (pdf) (updated, 11/05)
Handout #12: Problem Set 2 Solutions (ps) (pdf)
Handout #13: Problem Set 4 (ps) (pdf) code for q4 code for q6 (corrected Octave bug, 11/26)

Note: this is due on Monday, December 1

Handout #14: Midterm Solutions (ps) (pdf)
Handout #15: Problem Set 3 Solutions (ps) (pdf)
Handout #16: List of related AI classes (ps) (pdf)
Handout #17: Machine Learning: Other readings (ps) (pdf)
Handout #18: Problem Set 4 Solutions (ps) (pdf)

Lecture Notes

Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms
Lecture notes 2 (ps) (pdf) Generative Algorithms
Lecture notes 3 (ps) (pdf) Support Vector Machines
Lecture notes 4 (ps) (pdf) Learning Theory
Lecture notes 5 (ps) (pdf) Regularization and Model Selection
Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm. (optional reading)
Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering.
Lecture notes 7b (ps) (pdf) Mixture of Gaussians
Lecture notes 8 (ps) (pdf) The EM Algorithm
Lecture notes 9 (ps) (pdf) Factor Analysis
Lecture notes 10 (ps) (pdf) Principal Components Analysis
Lecture notes 11 (ps) (pdf) Independent Components Analysis
Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control

Section Notes

Section notes 1 (ps) (pdf) Linear Algebra Review and Reference (updated, 10/7)
Section notes 2 (ps) (pdf) Probability Theory Review
Section notes 3a (ps) (pdf) Multivariate Gaussians
Section notes 3b Matlab Review
Section notes 4 (ps) (pdf) Convex Optimization Overview (part I) (matlab demo)
Section notes 5 (ps) (pdf) Convex Optimization Overview (part II)
Section notes 6 (ps) (pdf) Midterm review
Section notes 7 (ps) (pdf) More on Gaussians
Section notes 8 (ps) (pdf) Gaussian Processes (matlab demo)

Other resources

Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found here.

Previous projects: A list of last year's final projects can be found here.

Matlab resources: Here are a couple of Matlab tutorials that you might find helpful: http://www.math.ufl.edu/help/matlab-tutorial/ and http://www.math.mtu.edu/~msgocken/intro/node1.html. For emacs users only: If you plan to run Matlab in emacs, here are matlab.el, and a helpful .emac's file.

Octave resources: For a free alternative to Matlab, check out GNU Octave. The official documentation is available here. Some useful tutorials on Octave include http://homepages.nyu.edu/~kpl2/dsts6/octaveTutorial.html and http://wiki.aims.ac.za/mediawiki/index.php/Octave.

Data: Here is the UCI Machine learning repository, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences NIPS (all old NIPS papers are online) and ICML. Some other related conferences include UAI, AAAI, IJCAI.

Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a PostScript viewer or PDF viewer for it if you don't already have one.

Comments to cs229-qa@cs.stanford.edu

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