learning vector quantization造句
例句與造句
- a stochastic competitive learning vector quantization algorithm for image coding
一種隨機(jī)競(jìng)爭(zhēng)學(xué)習(xí)矢量量化圖像編碼算法 - the purpose of this study was to explore the effect of the different training samples, learning rate and numbers of hidden layers on the classified accuracy when using learning vector quantization to analysis
摘要本研究采電腦模擬探討不同訓(xùn)練范例樣本數(shù)、學(xué)習(xí)速率及隱藏層個(gè)數(shù)對(duì)學(xué)習(xí)向量量化網(wǎng)路分類正確率之影響。 - the purpose of this study was to explore the effect of the different training samples, learning rate and numbers of hidden layers on the classified accuracy when using learning vector quantization to analysis
摘要本研究采計(jì)算機(jī)仿真探討不同訓(xùn)練示范樣本數(shù)、學(xué)習(xí)速率及隱藏層個(gè)數(shù)對(duì)學(xué)習(xí)矢量量化網(wǎng)絡(luò)分類正確率之影響。 - this method combines a genetic algorithm with an artificial neural network classifier, such as back-propagation ( bp ) neural classifier, radial basis function ( rbf ) classifier or learning vector quantization ( lvq ) classifier
此方法結(jié)合基因演算法與類神經(jīng)分類器，如倒傳遞分類器、放射基底函數(shù)分類器以及學(xué)習(xí)矢量量化分類器。 - secondly, a multilayered neural network trained with a learning vector quantization ( lvq ) algorithm is applied to pattern recognition of manifestations of the pulse and the classification ability of lvq network is compared with traditional near neighbor algorithm
其次，本文根據(jù)脈圖的時(shí)域特征，采用學(xué)習(xí)矢量量化算法，訓(xùn)練文中確立的神經(jīng)網(wǎng)絡(luò)分類器，用以實(shí)現(xiàn)對(duì)脈圖的識(shí)別。并比較了lvq神經(jīng)網(wǎng)絡(luò)分類器與傳統(tǒng)近鄰法的分類性能。 - It's difficult to find learning vector quantization in a sentence. 用learning vector quantization造句挺難的
- the main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed; the xor problem is implemented by using pnn . a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons . simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn
本文主要分析了pnn隱層神經(jīng)元個(gè)數(shù),隱中心矢量,平滑參數(shù)等要素對(duì)網(wǎng)絡(luò)分類效果的影響,并用pnn實(shí)現(xiàn)了異或邏輯問(wèn)題;提出了一種新的pnn有監(jiān)督學(xué)習(xí)算法:用學(xué)習(xí)矢量量化對(duì)各類訓(xùn)練樣本進(jìn)行聚類,對(duì)平滑參數(shù)和距離各類模式中心最近的聚類點(diǎn)構(gòu)造區(qū)域,并采用遺傳算法在構(gòu)造的區(qū)域內(nèi)訓(xùn)練網(wǎng)絡(luò),實(shí)驗(yàn)表明:該算法在分類效果上優(yōu)于其它pnn學(xué)習(xí)算法