Back

Modern Machine Learning: Partition & Vote

Priebe, C. E.; Vogelstein, J.; Engert, F.; White, C. M.

2020-09-19 neuroscience
10.1101/2020.04.29.068460 bioRxiv
Show abstract

We present modern machine learning, focusing on the state-of-the-art classification methods of decision forests and deep networks, as partition and vote schemes. This illustrative presentation allows for both a unified basic understanding of how these methods work from the perspective of classical statistical pattern recognition as well as useful basic insight into their relationship with each other ... and potentially with brain functioning.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
PLOS Computational Biology
● 1863 papers in training set
Top 2%
15.5%
Similar papers in this journal
2
Neural Networks
○ 35 papers in training set
Top 0.1%
10.1%
Similar papers in this journal
3
Neural Computation
● 39 papers in training set
Top 0.1%
9.9%
Similar papers in this journal
4
Frontiers in Artificial Intelligence
○ 20 papers in training set
Top 0.1%
9.9%
Similar papers in this journal
5
Frontiers in Computational Neuroscience
○ 60 papers in training set
Top 0.2%
5.3%
Similar papers in this journal
50% of probability mass above
6
eLife
● 5828 papers in training set
Top 32%
3.5%
7
Scientific Reports
○ 3612 papers in training set
Top 40%
2.5%
8
Nature Communications
○ 5641 papers in training set
Top 39%
2.5%
9
NeuroImage
○ 903 papers in training set
Top 4%
2.0%
10
Frontiers in Neuroinformatics
○ 41 papers in training set
Top 0.3%
1.8%
11
Patterns
○ 78 papers in training set
Top 1%
1.8%
12
Biostatistics
◐ 24 papers in training set
Top 0.2%
1.7%
13
Neuroinformatics
○ 46 papers in training set
Top 0.5%
1.5%
14
Psychological Review
● 19 papers in training set
Top 0.1%
1.5%
15
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 30%
1.5%
16
Network Neuroscience
● 126 papers in training set
Top 1.0%
1.5%
17
PLOS ONE
● 5266 papers in training set
Top 50%
1.4%
18
Heliyon
○ 152 papers in training set
Top 6%
1.1%
19
Journal of Neural Engineering
○ 221 papers in training set
Top 2%
0.9%
20
eneuro
● 439 papers in training set
Top 7%
0.9%
21
Medical Image Analysis
○ 35 papers in training set
Top 0.6%
0.9%
22
Biological Cybernetics
○ 15 papers in training set
Top 0.2%
0.9%
23
Journal of Neurophysiology
● 302 papers in training set
Top 3%
0.9%
24
Journal of Vision
● 110 papers in training set
Top 0.8%
0.6%
25
Frontiers in Neuroscience
○ 256 papers in training set
Top 7%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.