登录
首页 » matlab » gpml-matlab-v1.3-2006-09-08

gpml-matlab-v1.3-2006-09-08

于 2020-02-26 发布
0 353
下载积分: 1 下载次数: 3

代码说明:

说明:  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社,2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性,首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次,它允许指定不同的推理过程,如精确推理和期望传播(EP)。第三,它允许指定似然函数,如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的,允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)

文件列表:

gpml-matlab, 0 , 2006-09-08
gpml-matlab\README, 3741 , 2006-09-08
gpml-matlab\doc, 0 , 2006-03-29
gpml-matlab\doc\classification.html, 34439 , 2006-03-27
gpml-matlab\doc\alg31.gif, 43172 , 2005-11-07
gpml-matlab\doc\style.css, 77 , 2006-01-30
gpml-matlab\doc\alg32.gif, 34064 , 2005-11-07
gpml-matlab\doc\index.html, 3349 , 2006-03-29
gpml-matlab\doc\alg35.gif, 66673 , 2006-02-10
gpml-matlab\doc\alg36.gif, 32226 , 2006-02-10
gpml-matlab\doc\alg51.gif, 60084 , 2006-01-31
gpml-matlab\doc\alg52.gif, 41085 , 2006-02-10
gpml-matlab\doc\figepp.gif, 17875 , 2006-03-10
gpml-matlab\doc\figepp2.gif, 23299 , 2006-03-10
gpml-matlab\doc\figlapp.gif, 17854 , 2006-03-09
gpml-matlab\doc\figlapp2.gif, 25557 , 2006-03-09
gpml-matlab\doc\regression.html, 16598 , 2006-03-29
gpml-matlab\doc\figl1.gif, 21386 , 2005-10-26
gpml-matlab\doc\alg21.gif, 31996 , 2005-10-26
gpml-matlab\doc\sparse-approx.html, 7738 , 2006-03-29
gpml-matlab\doc\fig2d.gif, 36772 , 2006-03-14
gpml-matlab\doc\fig2de1.gif, 35364 , 2006-03-14
gpml-matlab\doc\figlf.gif, 16078 , 2006-03-27
gpml-matlab\doc\fig2de2.gif, 42683 , 2006-03-14
gpml-matlab\doc\fig2de3.gif, 38483 , 2006-03-14
gpml-matlab\doc\fig2dl1.gif, 34059 , 2006-03-14
gpml-matlab\doc\fig2dl2.gif, 40431 , 2006-03-14
gpml-matlab\doc\fig2dl3.gif, 38375 , 2006-03-14
gpml-matlab\doc\figl.gif, 6109 , 2006-03-27
gpml-matlab\doc\figlm.gif, 16712 , 2006-03-27
gpml-matlab\gpml, 0 , 2006-09-08
gpml-matlab\gpml\binaryEPGP.m, 7565 , 2006-09-08
gpml-matlab\gpml\binaryLaplaceGP.m, 7940 , 2006-05-10
gpml-matlab\gpml\Contents.m, 2206 , 2006-04-07
gpml-matlab\gpml\Copyright, 775 , 2006-02-09
gpml-matlab\gpml\gprSRPP.m, 2963 , 2006-03-30
gpml-matlab\gpml\minimize.m, 8995 , 2006-09-08
gpml-matlab\gpml\solve_chol.c, 1236 , 2006-02-09
gpml-matlab\gpml\solve_chol.m, 991 , 2006-02-08
gpml-matlab\gpml\sq_dist.c, 1931 , 2006-02-08
gpml-matlab\gpml\sq_dist.m, 2186 , 2006-03-09
gpml-matlab\gpml\covMatern5iso.m, 1221 , 2006-03-24
gpml-matlab\gpml\covRQiso.m, 1486 , 2006-09-08
gpml-matlab\gpml\covMatern3iso.m, 1206 , 2006-03-24
gpml-matlab\gpml\covSEiso.m, 1349 , 2006-04-07
gpml-matlab\gpml\Makefile, 131 , 2006-03-29
gpml-matlab\gpml\covFunctions.m, 4136 , 2006-05-15
gpml-matlab\gpml\gpr.m, 2811 , 2006-03-27
gpml-matlab\gpml\covSEard.m, 1569 , 2006-03-24
gpml-matlab\gpml\covProd.m, 2582 , 2006-04-06
gpml-matlab\gpml\covLINone.m, 984 , 2006-03-27
gpml-matlab\gpml\covSum.m, 2400 , 2006-03-20
gpml-matlab\gpml\covNoise.m, 1065 , 2006-03-24
gpml-matlab\gpml\covNNone.m, 1778 , 2006-03-24
gpml-matlab\gpml\covRQard.m, 1660 , 2006-09-08
gpml-matlab\gpml\covPeriodic.m, 1085 , 2006-04-07
gpml-matlab\gpml\covLINard.m, 1046 , 2006-03-24
gpml-matlab\gpml\covConst.m, 758 , 2006-03-24
gpml-matlab\gpml-demo, 0 , 2006-03-29
gpml-matlab\gpml-demo\Contents.m, 791 , 2006-03-27
gpml-matlab\gpml-demo\data_6darm.mat, 22768 , 2006-02-27
gpml-matlab\gpml-demo\data_boston.mat, 57040 , 2006-03-29
gpml-matlab\gpml-demo\demo_ep_2d.m, 7012 , 2006-03-29
gpml-matlab\gpml-demo\demo_ep_usps.m, 6130 , 2006-03-29
gpml-matlab\gpml-demo\demo_gparm.m, 8178 , 2006-03-29
gpml-matlab\gpml-demo\demo_gprsparse.m, 6511 , 2006-03-29
gpml-matlab\gpml-demo\demo_laplace_2d.m, 7257 , 2006-03-29
gpml-matlab\gpml-demo\demo_laplace_usps.m, 6134 , 2006-03-29
gpml-matlab\gpml-demo\demo_gpr.m, 9382 , 2006-03-29

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • SceneClass13
    DataSet:SceneClass13 (bedroom,coast,country,office,mountain,forest,livingroom and so on)
    2010-05-14 23:34:07下载
    积分:1
  • matlab
    主要是关于一些matlab的常用函数的说明,其实用途(some matlab function)
    2011-09-11 22:34:07下载
    积分:1
  • Discharging_Capacitor_exp_108b
    Practical Experiment for Discharging a capacitor in MAtlab
    2013-10-04 15:04:41下载
    积分:1
  • traffic-code
    Real-time Recognition System of Traffic Light in Urban Environment Abstract—detection of arrow traffic light is a focal point research in autonomous vehicle, and in urban environment it is the basic technique. However, most researches mainly concern the circular traffic lights. A novel algorithm is proposed in this paper to resolve the problems of detection and recognition of arrow traffic lights.
    2013-11-20 18:59:20下载
    积分:1
  • mygui
    matlab中对GUI的操作,熟悉在GUI中控件的操作(matlab in the operation of the GUI, and familiar with the operation of the control in the GUI)
    2014-01-03 15:09:22下载
    积分:1
  • imm_Kalman
    交互式多模型卡尔曼滤波算法,适应于道路交通和航迹跟踪问题(Interactive Multi model kalman filter algorithm )
    2021-01-04 11:28:55下载
    积分:1
  • matlab_bgl-4.0.1
    功能十分强大的图论工具箱,切实可靠,能用,十分不错。(Graph Theory Toolbox)
    2014-02-07 19:01:36下载
    积分:1
  • matlab2
    说明:  该程序是在matlab中利用窗函数法设计滤波器的示例(The program is using the window function matlab filter design example)
    2011-04-16 16:09:41下载
    积分:1
  • MATLAB
    matlab模拟的滤波器 高通 低通 带通 带限 各种滤波器应有尽有 非常齐全 物超所值(Matlab simulation filter high-pass low-pass band-pass band-limited Various filters on everything)
    2012-04-11 10:01:18下载
    积分:1
  • swelden
    利用Swelden算法实现信号的分解与重构,使用haar小波进行一次提升(Algorithm for signal decomposition and reconstruction)
    2012-05-09 19:15:47下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载