statistic samples 中文意思是什麼
statistic samples
解釋
統計樣本-
In algorithms, classification algorithms are divided into two cases : one for known statistical distribution model and the other for unknown statistical distribution model. four classification algorithms, the bata - prime statistic model fusing quadratic gamma classifier, based on sar image rcs reconstruction and space position mode, on the mixed double hint layers rbfn ( mdhrbfn ) model and on the self - adapt fuzzy rbfn ( afrbfn ) model, are derived. the problems, including how to further improving the class ratio of the bayes decision, decreasing the dependence on the statistical model and directly providing the adapted algorithm with samples, are solved
提出了基於徑向基函數神經網路（ rbfn ）的雙隱層混合網路（ mdhrbfn ）模型，解決了標準神經網路在具體sar圖像地物分類中分類類別數目不夠和分類精度差的問題；提出了基於模糊推理系統的自適應模糊rbfn分類（ afrbfn ）模型，兼顧通用性與精確性，增強人機交互能力，進一步提高了演算法分類率。 -
Its core arithmetic is i d3 and ftart. to guarantee the sample data as science, entireness, typical, and accurate, the author selected the samples from the investigating network of shanghai statistic bureau, which were composed of 300 households in total and 292 effective samples were defined finally. the research lasted a period of one year and data collected within each season
為確保研究樣本數據的科學性、全面性、典型性和準確性，筆者結合由本人主持完成的香港理工大學itc的「中國家庭服裝消費結構狀況及影響因素」的課題研究，採用了上海統計局家庭計劃調查網路，抽取樣本300戶，回收有效問卷292份，整個調查歷時一年，分四季完成。 -
Compared with the classical bp algorithm, robust adaptive bp algorithm possesses some advantages as following : ( 1 ) increasing the accuracy of the network training by means of using both the relative and absolute residual to adjust the weight values ; ( 2 ) improve the robustness and the network convergence rate through combining with the robust statistic technique by way of judging the values of the samples " relative residual to establish the energy function so that can suppress the effect on network training because of the samples with high noise disturbances ; ( 3 ) prevent entrapping into the local minima area and obtain the global optimal result owing to setting the learning rate to be the function of the errors and the error gradients when network is trained. the learning rate of the weights update change with the error values of the network adaptively so that can easily get rid of the disadvantage of the classical bp algorithm that is liable to entrap into the local minima areas
與基本bp演算法相比，本文提出的魯棒自適應bp演算法具有以下優點： （ 1 ）與魯棒統計技術相結合，通過訓練樣本相對偏差的大小，確定不同訓練樣本對能量函數的貢獻，來抑制含高噪聲干擾樣本對網路訓練的不良影響，從而增強訓練的魯棒性，提高網路訓練的收斂速度； （ 2 ）採用相對偏差和絕對偏差兩種偏差形式對權值進行調整，提高了網路的訓練精度； （ 3 ）在採用梯度下降演算法對權值進行調整的基礎上，通過將學習速率設為訓練誤差及誤差梯度的特殊函數，使學習速率依賴于網路訓練時誤差瞬時的變化而自適應的改變，從而可以克服基本bp演算法容易陷入局部極小區域的弊端，使訓練過程能夠很快的「跳出」局部極小區域而達到全局最優。 -
The adaptation processing includes linear prediction coefficient adaptation and adaptation of quantization step size for residual signals. based on g. 726, we adopt a huffman coder to make use of probability statistic of bit cascade covering n ( n 1 ) samples generated from adpcm, in order to further reduce the bit rate. ng is lossless entropy coding, the speech quality of our improved algorithm should be same as that of g. 726 standard
我們的研究和改進工作包括:研究最優非均勻自適應量化器，及其自適應演算法;研究波形預測函數，以及函數零點、極點的自適應演算法;基於每n （ n 1 ）個樣本所對應符號的概率統計，對預測殘差量化值再進行huffman編碼，進一步降低比特率。 -
Using historical data from the family, statistic samples were amplified. the approach not only is effective in quality control of the process out, but also convenience and simple
這種方法既能有效的監控過程質量變異，又能方便簡捷的在現場操作的質量控制方法。 -
When the soil water properties and soil water - storage was studied with traditional statistic method, samples were entirely independent and obeyed normal distribution, not taking into account spatial relative of sampling location
在用傳統統計方法分析土壤水分特性和土壤水庫貯量時，根據finsher統計原理假設樣本之間完全獨立且服從正態分佈為前提，不考慮測定位置的空間關系。 -
Therefore the pvd will become another promising statistic to test galaxy formation models with redshift samples of lbgs
因此， pvd將會成為運用lymanbreak星系紅移巡天來約束星系形成模型的強有力的統計工具。 -
Four chapters including chapter 5 and chapter 6 and chapter 7 and chapter 8 were consisted of part 3 as empirical analysis. in order to improve reliability and classic and effectiveness of research conclusions to the greatest extents, characteristics of statistic samples and time periods were emphasized in the studies and the kinds of statistical software and test tools were used in the periods of models being built
第三部分為我國上市公司控制權變更的實證分析，包括第五章、第六章、第七章和第八章共四章內容，實證是該部分的最大特徵，統計樣本選取注重其代表性和時序發展特徵，統計指標全面和多角度反映研究對象，運用統計軟體和多種檢驗手段建立模型，以便最大限度提高研究結論的可靠性、代表性和有效性。 -
Based upon the deficiencies of the back propagation algorithm in the practical application, after some mechanisms effecting the network training and the other performances are analyzed when training samples with disturbance are employed in training, in this paper, through combining the chief thoughts of the classical bp algorithm and the robust statistic technique, improving the optimal algorithm of the bp algorithm, a new algorithm with high robustness - robust adaptive bp algorithm is proposed, and also make a good effect when integrated this new algorithm with the dynamical bp network to predict the stock price
本文從基本bp演算法在應用中存在的不足出發，著重分析了訓練樣本中所含噪聲對基本bp演算法在網路訓練過程中產生的不良影響，並以此為依據，採用魯棒統計技術，同時在優化演算法上做了一些有益的改進，提出一種新的具有較強抗干擾能力的bp演算法? ?魯棒自適應bp演算法，並將其應用於動態bp網路，進行股票價格的預測，取得了較好的預測效果。 -
Secondly, revise factor coefficient with probability distribution, which given by experienced experts. thirdly, use bayes statistic deducing method to bind together the income rate of prior distribution and sample in formation, which makes forecast stocks in shenzhen stock market as samples. work out the series of weakly income rate
（ 2 ）對多元回歸的因子模型的各因子權重重做修正，將一些對金融市場有較透徹了解和豐富經驗的專家提供的信息引入，作出因子系數的概率分佈（並非隨意的主觀臆造） ，對模型的結果加以修正，以便提高模型的準確度。 -
In this thesis, several issues concerning the machine learning and the classification of high dimensional multispectral data with limited training samples are addressed, which are based on statistic learning theory ( slt ), support vector machine ( svm ) and artificial neural networks ( ann ). the mai n work and results are outlined as follows : 1. the characteristics of high dimensional multispectral data are studied, and the difficulties that deteriorate the performance of the traditional pattern classification algorithms are carefully analyzed
以統計學習理論（ statisticlearningtheory ? slt ） 、支持向量機（ supportvectormachine ? svm ）和人工神經網路（ artificialneuralnetworks ? ann ）為基礎，本文開展了以下幾個方面的研究工作：深入分析了高維多光譜數據的特點和傳統模式分類方法在高維多光譜數據分類中面臨的困難。
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