-
Notifications
You must be signed in to change notification settings - Fork 0
/
multiview.html
25 lines (19 loc) · 1.83 KB
/
multiview.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
<h2 id="MVL">Multi-view learning for object classification</h2>
<img class="paper" title="ICML 2013" src="./page_files/multiview_pic_small.png"><br>
<a class="paper" href="http://lorisbaz.github.io/papers/proceedings/Minhetal_ICML13.pdf"> A unifying framework for vector-valued manifold regularization and multi-view learning</a><br>
H. Q. Minh, <strong>L. Bazzani</strong>, V. Murino<br>
The 30th International Conference on Machine Learning (ICML), 2013<br><br>
<a class="paper" href="http://lorisbaz.github.io/papers/proceedings/Minhetal_arxiv14.pdf">A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning</a><br>
H. Q. Minh, <strong>L. Bazzani</strong>, V. Murino<br>
Journal of Machine Learning Research (JMLR), 2016 <br>
<h3>Details</h3>
<div class="paper">
We propose a general vector-valued reproducing kernel Hilbert spaces formulation for the problem of learning an unknown functional dependency between a structured input space and a structured output space, in the Semi-Supervised Learning setting. In the case of least square loss function, we provide a closed form solution with an efficient implementation. Numerical experiments on challenging multi-class categorization problems show that our multi-view learning formulation achieves results which are comparable with state of the art and are significantly better than single-view learning. <br> <br>
<center>
<a href="http://github.com/lorisbaz/Multiview-learning"><img class="aligncenter wp-image-866" alt="multiview_pic" src="./page_files/multiview_pic_small.png" height="450"></a>
</center>
</div>
<div class="paper">
<center><h3><a href="http://github.com/lorisbaz/Multiview-learning">Download MVL</a></h3> (link to github)</center> <br>
See the instructions in the README.md file.
</div>