sparse data problem 中文意思是什麼
sparse data problem
解釋
稀疏數據問題-
On data level, it is firstly explored fusing signature data from sparse - band collocated radars to obtain wider band target frequency response. the most difficult problem which restricts the fusion process is the lack of mutual coherence between the various radar subbands
在數據層，首先探索了對多個配置在同一地方的不同頻帶雷達測得的目標頻率響應進行融合，以獲取更寬頻帶目標頻率響應的方法。 -
This problem arises from the circuit layout of vlsi designs, interconnection networks, sparse matrix computations, error - correcting code designs, data structures, biology, etc, which has extensive backgrounds
圖的嵌入問題是從稀疏矩陣的計算、數據結構、 vlsi電子線路設計和分子生物學等問題中提取出來的數學模型，有著廣泛的應用背景。 -
The kanerva ' s sparse distributed memory ( sdm ) tackles the problem of training large data patterns and extendes the storage mode of existing computer. but it ' s address array produced randomly ca n ' t reveal the distribution of patterns and it has ' t the ability of function approximation for its learning rule
Kanerva的稀疏分佈存儲（ sdm ）模型解決了大維數樣本的訓練問題，推廣了現有計算機的存儲方式。但其地址矩陣的隨機預置方式不能反映樣本的分佈，並且sdm的學習方式使之不能用於函數逼近及時間序列預測問題。 -
It adopts the hierachical clustering in vocabulary vsm model because of its special function, on the other hand enriches the subcategory tagging information by rules, it can decrease me data sparse problem, and introduces the confidence intervals into the model for the selection of priority between statistics and rules
另外還對標注模型從兩方面作了優化，由於詞匯特徵向量的特殊作用，本文對特徵詞匯採用層次聚類來提高其分類精度；另一方面，引入規則來進一步豐富細分類標注信息，減少數據稀疏等問題，並且引入置信度來選擇統計與規則的優先關系。 -
We introduce and motivate the main theme of the course, the setting of the problem of learning from examples as the problem of approximating a multivariate function from sparse data - the examples
我們介紹且激發課程的主題將朝向于實例學習法的問題設定，例如稀疏值中多變量函數近似的問題。 -
We look at the problem of learning from examples as the problem of multivariate function approximation from sparse chosen data, and then consider the case in which the data are drawn, instead of chosen, according to a probability measure
並檢視稀疏精選值中多變量函數近似法等這些從實例學習法所發現的問題，然後根據機率衡量，審思隨機獲得資料而非選定資料的案例。 -
It can find that the data is high dimension and sparse. we bring forward hsmbk and hssca algorithms to code with the problem
針對實驗數據的高維性、稀疏性等特徵，我們提出了hsmbk和hssca兩個聚類演算法。 -
One vital problem with text classification is how to reduce the number of labeled data while maintain the proper accuracy. this paper partly solves this problem from two different aspects. firstly, we want to deal with sparse training data by selecting high performance algorithm
一般分類器的精度隨著訓練文本的增多而提高，但人工分類好的文本是一種昂貴的資源，文本分類演算法要解決的一個重要問題是要減少訓練集中人工分類的文本數量，同時保證其精度。
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