Java发送带附件的邮件类。 对javax.mail的封装，很简单的调用。 只要传入smtp主机，用户名密码，附件路径，消息内容，就可以直接发送到对方的邮箱了。是使用java发送邮件的很好的学习资料。 注意要使用的库有mail.jar,activation.jar等。
上传时间： 2014-01-08
上传用户：bibirnovis
软件简介：HI-TECH PICC 是一款高效的C编译器，支持Microchip PICmicro 10/12/14/16/17系列控制器。是一款强劲的标准C编译器，完全遵守ISO/ANSI C，支持所有的数据类型包括24 and 32 bit IEEE 标准浮点类型。智能优化产生高质量的代码。属于第三方开发工具。能和MPLAB整合，内嵌开发环境（HI－TIDE）。 Hi-tech PICC Compiler v8.注册码 Serial: HCPIC-88888 First Name: ONE Last Name: TWO Company Name:ONE TWO Registration: 任意填，但一定要填 activation: NPCBACMJKLPCADKLOEDBFPIOCIBAEIDI
上传时间： 2016-12-16
上传用户：Andy123456
* Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network libraries. It features multilayer * feedforward perceptron neural networks, sigmoidal activation function * with bias, backpropagation training with settable learning rate and * momentum, and backpropagation training in batches.
标签： backpropagation implementating Lightweight lightweight
上传时间： 2013-12-27
上传用户：清风冷雨
Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％ ％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％
标签： back-propagation corresponding input-output algorithm
上传时间： 2016-12-26
上传用户：exxxds
％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms) ％ trains the network with the Levenberg-Marquardt method. ％ ％ The activation functions can be either linear or tanh. The ％ network architecture is defined by the matrix NetDef which ％ has two rows. The first row specifies the hidden layer and the ％ second row specifies the output layer.
标签： Levenberg-Marquardt desired network neural
上传时间： 2016-12-26
上传用户：jcljkh
Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defined by the matrix NetDef , which has of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer.
标签： recursive prediction algorithm Gauss-Ne
上传时间： 2016-12-26
上传用户：ljt101007
OReilly.Java.Rmithis book provides strategies for working with serialization, threading, the RMI registry, sockets and socket factories, activation, dynamic class downloading, HTTP tunneling, distributed garbage collection, JNDI, and CORBA. In short, a treasure trove of valuable RMI knowledge packed into one book.
标签： serialization strategies threading provides
上传时间： 2014-01-14
上传用户：731140412
在实际项目项目开发中，很多时候需要用到邮件，比如论坛注册需要用邮件激活。 一般用Javamail发送，目前最新的版本是1.4.2 可以在http://java.sun.com/products/javamail/index.jsp 下载最新版本 如果使用的不是J2SE6，那么需要把 JavaBeans activation Framework加到环境变量 可以在http://java.sun.com/javase/technologies/desktop/javabeans/jaf/index.jsp 下载 不过为了简化开发，可以直接使用apache common项目的mail 官方网站为: http://commons.apache.org/email/
标签： 项目
上传时间： 2014-02-13
上传用户：龙飞艇
NN Functions a program in Lisp to demonstrate working of an artificial neuron. (Enter an input vector X and weight vector W. Calculate weighted sum XW. Transform this using signal or activation functions like logistic, threshold, hyperbolic-tangent, linear, exponential, sigmoid or some other functions (syntax provided) and display the output).
标签： demonstrate artificial Functions program
上传时间： 2013-12-30
上传用户：hfmm633
ADIAL Basis Function (RBF) networks were introduced into the neural network literature by Broomhead and Lowe [1], which are motivated by observation on the local response in biologic neurons. Due to their better approximation capabilities, simpler network structures and faster learning algorithms, RBF networks have been widely applied in many science and engineering fields. RBF network is three layers feedback network, where each hidden unit implements a radial activation function and each output unit implements a weighted sum of hidden units’ outputs.
标签： introduced literature Broomhead Function
上传时间： 2017-08-08
上传用户：lingzhichao
