Title: Probability of Detection for Grab Sample Selection
person("Mayooran", "Thevaraja", email = "mayooran@eng.jfn.ac.lk", role = c("aut", "cre")),
person("Kondaswamy", "Govindaraju", email = "k.govindaraju@massey.ac.nz", role = "aut"),
person("Mark", "Bebbington", email = "m.bebbington@massey.ac.nz", role = "aut")
Description: Functions for obtaining the probability of detection, for grab samples selection by using two different methods such as systematic or random based on two-state Markov chain model. For detection probability calculation, we used results from Bhat, U. and Lal, R. (1988) <doi:10.2307/1427041>.
Imports: extraDistr, ggplot2, ggthemes, plyr, reshape2, stats

Package: gradDescent
Maintainer: Lala Septem Riza <lala.s.riza@upi.edu>
Type: Package
Title: Gradient Descent for Regression Tasks
Version: 2.0.1
URL: https://github.com/drizzersilverberg/gradDescentR
Date: 2017-3-11
Author: Dendi Handian, Imam Fachmi Nasrulloh, Lala Septem Riza
Description: An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks.
The variants of gradient descent algorithm are :
Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load.
Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically.
Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average.
Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning.
Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning.
Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.
License: GPL (>= 2) | file LICENSE
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-03-11 01:47:04 UTC; dendi
Repository: CRAN
Date/Publication: 2017-03-11 10:59:29