Java發送帶附件的郵件類。 對javax.mail的封裝，很簡單的調用。 只要傳入smtp主機，用戶名密碼，附件路徑，消息內容，就可以直接發送到對方的郵箱了。是使用java發送郵件的很好的學習資料。 注意要使用的庫有mail.jar,activation.jar等。
上傳時間： 2014-01-09
上傳用戶：bibirnovis
軟件簡介：HI-TECH PICC 是一款高效的C編譯器，支持Microchip PICmicro 10/12/14/16/17系列控制器。是一款強勁的標準C編譯器，完全遵守ISO/ANSI C，支持所有的數據類型包括24 and 32 bit IEEE 標準浮點類型。智能優化產生高質量的代碼。屬于第三方開發工具。能和MPLAB整合，內嵌開發環境（HI－TIDE）。 Hi-tech PICC Compiler v8.注冊碼 Serial: HCPIC-88888 First Name: ONE Last Name: TWO Company Name:ONE TWO Registration: 任意填，但一定要填 activation: NPCBACMJKLPCADKLOEDBFPIOCIBAEIDI
上傳時間： 2016-12-16
上傳用戶：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.
標簽： backpropagation implementating Lightweight lightweight
上傳時間： 2013-12-27
上傳用戶：清風冷雨
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. ％
標簽： back-propagation corresponding input-output algorithm
上傳時間： 2016-12-27
上傳用戶：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.
標簽： Levenberg-Marquardt desired network neural
上傳時間： 2016-12-27
上傳用戶：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.
標簽： recursive prediction algorithm Gauss-Ne
上傳時間： 2016-12-27
上傳用戶：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.
標簽： serialization strategies threading provides
上傳時間： 2014-01-15
上傳用戶：731140412
在實際項目項目開發中，很多時候需要用到郵件，比如論壇注冊需要用郵件激活。 一般用Javamail發送，目前最新的版本是1.4.2 可以在http://java.sun.com/products/javamail/index.jsp 下載最新版本 如果使用的不是J2SE6，那么需要把 JavaBeans activation Framework加到環境變量 可以在http://java.sun.com/javase/technologies/desktop/javabeans/jaf/index.jsp 下載 不過為了簡化開發，可以直接使用apache common項目的mail 官方網站為: http://commons.apache.org/email/
標簽： 項目
上傳時間： 2014-02-13
上傳用戶：龍飛艇
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).
標簽： demonstrate artificial Functions program
上傳時間： 2013-12-30
上傳用戶：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.
標簽： introduced literature Broomhead Function
上傳時間： 2017-08-08
上傳用戶：lingzhichao
