Convolutional(2,1,6) Encoder and soft decision Viterbi Decoder 剛才上載的有錯誤,已修正
標簽： Convolutional decision Encoder Decoder
上傳時間： 2016-01-14
上傳用戶：hoperingcong
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
主要是KNN（the k-nearest neighbor algorithm ）,LVQ1（learning vector quantization 1）, DSM（decision surface mapping）算法。 and a simple clustering algorithm.
標簽： quantization k-nearest algorithm decision
上傳時間： 2016-02-07
上傳用戶：zhyiroy
One kind of decision-making tree algorithm, can be seen as one kind data mining algorithm ,find the rule from large-scale data middle.
標簽： algorithm kind decision-making mining
上傳時間： 2014-01-13
上傳用戶：ruan2570406
A general decision rule for stochastic blind maximum-likelihood OSTBC detection is derived.
標簽： maximum-likelihood stochastic detection decision
上傳時間： 2013-12-04
上傳用戶：xcy122677
Computes BER v EbNo curve for convolutional encoding / soft decision Viterbi decoding scheme assuming BPSK. Brute force Monte Carlo approach is unsatisfactory (takes too long) to find the BER curve. The computation uses a quasi-analytic (QA) technique that relies on the estimation (approximate one) of the information-bits Weight Enumerating Function (WEF) using A simulation of the convolutional encoder. Once the WEF is estimated, the analytic formula for the BER is used.
標簽： convolutional Computes encoding decision
上傳時間： 2013-12-24
上傳用戶：咔樂塢
The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Process : finite horizon, value iteration, policy iteration, linear programming algorithms with some variants. The functions (m-functions) were developped with MATLAB v6.0 (one of the functions requires the Mathworks Optimization Toolbox) by the decision team of the Biometry and Artificial Intelligence Unit of INRA Toulouse (France). The version 2.0 (February 2005) handles sparse matrices and contains an example
標簽： discrete-time resolution functions Decision
上傳時間： 2014-01-01
上傳用戶：xuanjie
A Web Tutorial on Discrete Features of Bayes Decision Theory This applet allows for the calculation of the decision boundary given a three dimensional feature vector. Specifically, by stipulating the variables such as the priors, and the conditional likelihoods of each feature with respect to each class, the changing decision boundary will be displayed.
標簽： calculation Tutorial Discrete Decision
上傳時間： 2013-12-22
上傳用戶：hxy200501
Bayes Decision Procedure Bases on MATLAB Progame
標簽： Procedure Decision Progame MATLAB
上傳時間： 2014-07-13
上傳用戶：csgcd001
維特比譯碼程序。viterbi decoder with hard decision
標簽： decision viterbi decoder hard
上傳時間： 2016-06-04
上傳用戶：上善若水
