This repository has been archived by the owner. It is now read-only.
Permalink
Browse files

version bumped and added info about migration (#14)

  • Loading branch information...
StrikerRUS authored and TongZhang-ML committed Jul 14, 2018
1 parent 19e245b commit 48cfeafb9d19674b5b5af1c362db61fa563faa1b
Showing with 7 additions and 4 deletions.
  1. +2 −0 CHANGES
  2. +4 −3 README.md
  3. +1 −1 include/header.h
View
@@ -12,3 +12,5 @@
Added openmp support and multi-threading for discretization
Added loop unrolling and compilation option for simd optimization
0.6 (Feb 2018)
Fixed bug which led to program crash in case of usage of small samples weights
View
@@ -2,7 +2,9 @@
# FastRGF
### Multi-core implementation of Regularized Greedy Forest [RGF]
### Version 0.3 (Dec 2016) by Tong Zhang
### Version 0.6 (Feb 2018) by Tong Zhang
#### The active development of FastRGF is maintained now in [RGF-team repository](https://github.com/RGF-team/rgf/tree/master/FastRGF)
---------
#### 1. Introduction
@@ -17,7 +19,7 @@ The implementation employs the following conepts described in the **[RGF]** pape
- fully-corrective update
- greedy node expansion with trade-off between leaf node splitting for current tree and root splitting for new tree
However, various simplifications are made to accelerate the training speed. Therefore, unlike the original RGF program (see <http://stat.rutgers.edu/home/tzhang/software/rgf/>), this software does not reproduce the results in the paper.
However, various simplifications are made to accelerate the training speed. Therefore, unlike the original RGF program (see <http://tongzhang-ml.org/software/rgf/index.html>), this software does not reproduce the results in the paper.
The implementation of greedy tree node optimization employs second order Newton approximation for general loss functions. For logistic regression loss, which works especially well for many binary classification problems, this approach was considered in **[PL]**; for general loss functions, 2nd order approximation was considered in **[ZCS]**.
@@ -62,4 +64,3 @@ The software is distributed under the MIT license. Please read the file [LICENSE
**[PL]** Ping Li. Robust LogitBoost and Adaptive Base Class (ABC) LogitBoost, *UAI* 2010.
**[ZCS]** Zhaohui Zheng, Hongyuan Zha, Tong Zhang, Olivier Chapelle, Keke Chen, Gordon Sun. A general boosting method and its application to learning ranking functions for web search, *NIPS* 2007.
View
@@ -37,7 +37,7 @@
namespace rgf {
#define VER "version 0.4 (Aug 2017) by Tong Zhang"
#define VER "version 0.6 (Feb 2018) by Tong Zhang"
const int max_thrds=128;

0 comments on commit 48cfeaf

Please sign in to comment.