搜索资源列表
smo.tar
- This an implementation of SMO type classifier[1]. The input file is "i_x.mat" which is the matrix form of ionoshpere data obtained from https://archive.ics.uci.edu/ml/datasets.html [1] http://www.csie.ntu.edu.tw/~cjlin/papers/quadworkset.pdf-Thi
lz4-delphi-master
- Delphi bindings for [lz4](https://code.google.com/p/lz4/). Includes easy-to-use wrapper class and an default mode implementation of the new [lz4s streaming format](http://fastcompression.blogspot.fr/2013/04/lz4-streaming-format-final.html).
DeepLearnToolbox_CNN_lzbV2.0
- DeepLearnToolbox_CNN_lzbV2.0 深度学习,卷积神经网络,Matlab工具箱 参考文献: [1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006 [2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998 [3] https://github.com/rasmusberg
DeepLearnToolbox_CNN_lzbV3.0
- CNN - 主程序 参考文献: [1] Notes on Convolutional Neural Networks. Jake Bouvrie. 2006 [2] Gradient-Based Learning Applied to Document Recognition. Yann LeCun. 1998 [3] https://github.com/rasmusbergpalm/DeepLearnToolbox 作者：陆振波 电子
GBFMT-Image-Denoising-Codes
- This software release consists of an implementation of the algorithm described in the paper: B. K. Shreyamsha Kumar, “Image Denoising based on Gaussian/Bilateral Filter and its Method Noise Thresholding , Signal, Image and Video Processing, pp. 1-1
hyperopt-hyperopt-0.1-2-gba2fe77
- HyperOpt：分布式异步超参数优化 HyperOpt是串行和并行优化Python库用于搜索空间，它可以包括实值的，离散的，有条件的维度.(hyperopt: Distributed Asynchronous Hyper-parameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may incl
hyperopt参数调优
- HyperOpt：分布式异步超参数优化 HyperOpt是串行和并行优化Python库用于搜索空间，它可以包括实值的，离散的，有条件的维度.(hyperopt: Distributed Asynchronous Hyper-parameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which may incl
CNN_Torch7-master
- 在ubuntu下实现cnn网络，有相关数据集(CNN_Torch7 ========== This code use the code of Supervised Learning tutorial of Torch7. I add the loading of image by using graphicsMagick for Torch7. 1. for the code intepretation: http://code.madbits.com/wiki/doku.php?id