-
1
-
-
38949193299
-
Why is real-world visual object recognition hard?
-
N. Pinto, D.O. Cox, and J.J. DiCarlo, "Why is real-world visual object recognition hard?, " PLoS computational biology, vol. 4, no. 1, pp. e27, 2008.
-
(2008)
PLoS Computational Biology
, vol.4
, Issue.1
-
-
Pinto, N.1
Cox, D.O.2
Dicarlo, J.J.3
-
3
-
-
79955445232
-
Visual object recognition
-
Kristen Grauman and Bastian Leibe, "Visual object recognition, " Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 5, no. 2, pp. 1-181, 2011.
-
(2011)
Synthesis Lectures on Artificial Intelligence and Machine Learning
, vol.5
, Issue.2
, pp. 1-181
-
-
Grauman, K.1
Leibe, B.2
-
4
-
-
0034220148
-
Category learning with minimal prior knowledge
-
A.S. Kaplan and G.L. Murphy, "Category learning with minimal prior knowledge., " Journal of Experimental Psychology: Learning, Memo/y, and Cognition, vol. 26, no. 4, pp. 829, 2000.
-
(2000)
Journal of Experimental Psychology: Learning, Memo/y, and Cognition
, vol.26
, Issue.4
, pp. 829
-
-
Kaplan, A.S.1
Murphy, G.L.2
-
5
-
-
84970417663
-
Interest, prior knowledge, and learning
-
S. Tobias, "Interest, prior knowledge, and learning, " Review of Educational Research, vol. 64, no. I, pp. 37, 1994 .
-
(1994)
Review of Educational Research
, vol.64
, Issue.1
, pp. 37
-
-
Tobias, S.1
-
6
-
-
34047174674
-
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
-
L. Fei-Fei, R. Fergus, and P. Perona, "Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories, " Computer Vision and image Understanding, vol. 106, no. 1, pp. 59-70, 2007.
-
(2007)
Computer Vision and Image Understanding
, vol.106
, Issue.1
, pp. 59-70
-
-
Fei-Fei, L.1
Fergus, R.2
Perona, P.3
-
7
-
-
34948904828
-
Caltech-256 object category dataset
-
California Institute of Technology
-
G. Griffin, A Holub, and P. Perona, "Caltech-256 object category dataset, " Tech. Rep. 7694, California Institute of Technology, 2007.
-
(2007)
Tech. Rep
, vol.7694
-
-
Griffin, G.1
Holub, A.2
Perona, P.3
-
8
-
-
56749117943
-
In defense of one-vs-all classification
-
R. Rifkin and A. Klautau, "In defense of one-vs-all classification, " JMLR, vol. 5, pp. 101-141, 2004.
-
(2004)
JMLR
, vol.5
, pp. 101-141
-
-
Rifkin, R.1
Klautau, A.2
-
9
-
-
50949133669
-
Liblinear: A library for large linear classification
-
R.E. Fan, K.W. Chang, C.1. Hsieh, X.R. Wang, and C.1. Lin, "Liblinear: A library for large linear classification, " JMLR, vol. 9, pp. 1871-1874, 2008.
-
(2008)
JMLR
, vol.9
, pp. 1871-1874
-
-
Fan, R.E.1
Chang, C.I.2
Hsieh, X.R.3
Wang, K.W.4
Lin, C.I.5
-
10
-
-
77956195198
-
Large linear classification when data cannot fit in memory
-
ACM
-
H.F. Yu, C.1. Hsieh, K.W. Chang, and C.1. Lin, "Large linear classification when data cannot fit in memory, " in ACM SIGKDD. ACM, 2010, pp. 833-842.
-
(2010)
ACM SIGKDD
, pp. 833-842
-
-
Yu, H.F.1
Hsieh, C.I.2
Chang, K.W.3
Lin, C.I.4
-
11
-
-
12244257798
-
Semantic representation: Search and mining of multimedia content
-
AP. Natsev, M.R. Naphade, and J.R. Smith, "Semantic representation: search and mining of multimedia content, " in SiGKDD, 2004, pp. 641-646.
-
(2004)
SiGKDD
, pp. 641-646
-
-
Natsev, A.P.1
Naphade, M.R.2
Smith, J.R.3
-
12
-
-
36849011561
-
Model-shared subspace boosting for multi-label classification
-
R. Yan, 1. Tesic, and 1.R. Smith, "Model-shared subspace boosting for multi-label classification, " in ACM SiGKDD, 2007, pp. 834-843.
-
(2007)
ACM SiGKDD
, pp. 834-843
-
-
Yan, R.1
Tesic, I.2
Smith, I.R.3
-
13
-
-
33747626730
-
Large-scale concept ontology for multimedia
-
M. Naphade, J.R. Smith, J. Tesic, S.F. Chang, W. Hsu, L. Kennedy, A. Hauptmann, and J. Curtis, "Large-scale concept ontology for multimedia, " iEEE Multimedia, vol. 13, no. 3, pp. 86-91, 2006.
-
(2006)
IEEE Multimedia
, vol.13
, Issue.3
, pp. 86-91
-
-
Naphade, M.1
Smith, J.R.2
Tesic, J.3
Chang, S.F.4
Hsu, W.5
Kennedy, L.6
Hauptmann, A.7
Curtis, J.8
-
14
-
-
77951298115
-
The pascal visual object classes (VOC) challenge
-
M.E.L. Van and A. Zisserrnan, "The pascal visual object classes (VOC) challenge, " JJCV, vol. 88, no. 3, pp. 3-338, 2010.
-
(2010)
JJCV
, vol.88
, Issue.3
, pp. 3-338
-
-
Van, M.E.L.1
Zisserrnan, A.2
-
15
-
-
85198028989
-
Imagenet: A large-scale hierarchical image database
-
J. Deng, W. Dong, R. Socher, L.I. Li, K. Li, and 1. Fei-Fei, "Imagenet: A large-scale hierarchical image database, " in CVPR, 2009, pp. 248-255.
-
(2009)
CVPR
, pp. 248-255
-
-
Deng, J.1
Dong, W.2
Socher, R.3
Li, K.4
Li, L.I.5
Fei-Fei, I.6
-
16
-
-
51949098112
-
Classification using intersection kernel support vector machines is efficient
-
S. Maji, A.C. Berg, and 1. Malik, "Classification using intersection kernel support vector machines is efficient, " in C VP R, 2008.
-
(2008)
C VP R
-
-
Maji, S.1
Berg, A.C.2
Malik, I.3
-
17
-
-
0030211964
-
Bagging predictors
-
I. Breiman, "Bagging predictors, " Machine learning, vol. 24, no. 2, pp. 123-140, 1996.
-
(1996)
Machine Learning
, vol.24
, Issue.2
, pp. 123-140
-
-
Breiman, I.1
-
18
-
-
0037186544
-
Stochastic gradient boosting
-
J.H. Friedman, "Stochastic gradient boosting, " Computational Statistics & Data Analysis, vol. 38, no. 4, pp. 367-378, 2002.
-
(2002)
Computational Statistics & Data Analysis
, vol.38
, Issue.4
, pp. 367-378
-
-
Friedman, J.H.1
|