unsupervised 中文意思是什麼
unsupervised
解釋
無監督的-
In this paper, we made an investigation into texture feature extraction and classification based on statistic method and its application in multi - spectral image classification. the research works of this paper have been done as follows : firstly, in order to overcome the weakness of gray level co - occurrence matrix ( glcm ), a new unsupervised texture segment algorithm, based on multi - resolution model, is presented in this thesis
本文主要研究了基於紋理統計特性的特徵提取與分割方法，並將其用於實際的多光譜圖像分類，具體工作如下:第一，針對傳統灰度共現陣方法中特徵提取的尺度單一問題，本文提出了一種多分辨無監督紋理分割演算法。 -
The vegetation type was classified in the east transect by unsupervised classification using the data ( ikmx 1km ) from noaa meteorology satellite from 1995 - 1996, and the article analyzed the change of ndvi of all kinds of vegetation types, in the same time analyzed the forest dynamics in typical ecotone ( warm temperate zone to semitropical in qinling woods ) using the higher spatial resolution tm data
本論文利用1995 - 1996年noaa氣象衛星的ndvi （ 1km 1km ）數據，採用無監督分類方法對中國東部樣帶南方部分進行植被類型的劃分，分析各植被類型的ndvi變化情況；並利用較高精度的tm數據分析典型交錯區域（暖溫帶到亞熱帶的秦嶺林區）的森林動態變化情況。 -
Based on unsupervised learning, sparse coding is suitable to describe images with non - gaussian distribution and can get rid of the high order redundancy among the image pixels. since the basis function of sparse coding has build - in clustering property, it increases the inter - class variations of the features
稀疏編碼是一種基於非監督學習的演算法，它適合描述具有非高斯分佈的數據對象，能夠有效地消除圖像象素點之間的冗餘，並具有內在的聚類特性。 -
1, q 3, and at last prove the exisitence of ( q, m + n, n, m ) resilient functions when n > q ? 1. intelligentized ids methods, which can make the system more adaptability and self - studying, are important research directions of ids so far. in order to make the ids systems have better identifying ability and efficiency against new intrusions, we propose the intrusion feature extra - ction algorithm based on ikpca by studying the different kinds of intrusion detection feature extraction algorithm based on unsupervised learning, and then theoretically analysis the conver - gence of the algorithm. in addition, we validate the validity of the algorithm by means of experim - ents ; at the same time, through studying ica and neural networks, we propose fastica - nn ids, and then test the kddcup99 10 % date set to make comparison of kpca 、 ikpca and fastica algorithms in intrusion detection advantages and disadvantages
為了使入侵檢測系統對新的入侵行為有更好的識別能力和識別效率，本文在研究了各種基於無監督學習的入侵檢測特徵提取方法的基礎上，提出了基於增量核主成份分析（ ikpca ）的入侵檢測特徵提取方法，並對該方法進行了收斂性分析，同時結合模擬試驗對其正確性進行了驗證;另外，本文通過研究獨立成份分析和神經網路，提出了基於快速獨立成份分析和神經網路的入侵檢測方法（ fastica - nnids ） ，並通過對kddcup99的10 %數據集的檢測比較了核主成份分析（ kpca ） 、增量核主成份分析（ ikpca ）和快速獨立成份分析（ fastica ）在入侵檢測特徵提取方面的優缺點。 -
Research on unsupervised chinese segmentation based on em algorithm
一種改進的漢語分詞演算法 -
The multiscale mixed distribution models ( mmdm ) and the multiscale autoregressive ( mar ) models are investigated in this thesis, and they are applied to the unsupervised segmentation of the synthetic aperture radar ( sar ) image by joining them together - the multiscale mixed distribution models as the feature extractor and the multiscale autoregressive models as the classifier
本文對多尺度混合分佈模型（ multiscalemixturedistributionmodels簡記mmdm ） ，其中主要是對多尺度混合gauss分佈模型（ multiscalemixturegaussianmodels簡記mmgm ）和多尺度混合rayleigh分佈模型（ multiscalemixturerayleighmodels簡記mmrm ）進行了研究，及對多尺度自回歸（ multiscaleautoregressive簡記mar ）模型進行了研究，並將mmdm作為圖像分割的分類器， mar模型作為圖像分割的特徵提取器對合成孔徑雷達（ syntheticapertureradar簡記sar ）圖像無監督分割進行了研究。 -
Keywords : unsupervised artificial neural network, image identification, instantaneous velocity
關鍵詞：無監督式類神經網路、影像辨識、瞬時速度。 -
The former belongs to supervised learning and the latter belongs to unsupervised learning
它們分屬于有監督學習與無監督學習。 -
Due to its unsupervised learning ability, clustering has been widely used in numerous applications, such as pattern recognition, image processing, market research and so on
聚類具有無監督學習能力，被廣泛應用於多個領域中，如模式識別、數據分析、圖像處理以及市場調研等。 -
Approaches of immune computing, namely the aine model for unsupervised learning, airs model for supervised learning and the improved model of negative selection algorithm are exploited in an integrated way
綜合運用aine無監督學習模型、 airs有監督學習模型和文中給出的陰性選擇演算法改進模型，提出了基於免疫計算的機構軌跡綜合方法。 -
The supervised and unsupervised learning diagnosis methods are discussed and several improvements have been presented in the learning algorithms. the simulation results show that the proposed method can perforfti correct diagtioals iii the linear analog circuits with tolerances
本文對模擬故障診斷的有監督學習和無監督學習方法分別進行了研究，通過對實現過程的分析，對經典的學習演算法進行深入研究，並提出若干改進。 -
A series methods of data combination analyzing are selected to form the operating method system for crop discrimination. combining gis, gps, and other data from field work with rs data can determine interpretation features and set off working regions, combining rs data can enhance spatial features in order to do unsupervised classification efficiently, union of gis data enable us to join maps and extract features, to analyze crop structure, crop calendar, cultivating system
本項研究篩選出了構成運行化作物遙感識別技術體系的一系列數據復合分析方法，包括gps 、 gis數據以及其它田間作業信息與rs數據之間的復合，確立解譯標志和劃分作業區； rs數據之間的復合，進行圖像增強，改善非監督分類效果； gis數據之間的復合，分析作業區作物結構、物候和耕作制度現狀，地圖拼接、特徵提取等。 -
3. introducing the concept of equivalent pseudowords and the method of its construction, and achieving unsupervised wsd method by them
3 .提出等價偽詞概念和等價偽詞的構造方法，並以此實現無指導的詞義消歧方法。 -
In brief, the article has done some useful attempts in machine learning and unsupervised wsd methods, and gets some initial findings. with devotion of
綜上所述，本文在機器學習和無指導的詞義消歧方法上都作了一些有益的嘗試，取得了一些初步成果。 -
The main emphasis of our research is statistical word sense disambiguation, which can be classified into two categories according different discipline methods : supervised and unsupervised
本文研究的重點在於統計詞義消歧技術，它根據使用的訓練方法的不同可以分為有指導和無指導的兩大類。 -
The experiment introduces that the concept of equivalent pseudowords and unsupervised wsd technology based on equivalent pseudowords provide a new thought and method for exploring the new technology of wsd
實驗表明等價偽詞的概念以及建立在等價偽詞基礎上的無指導詞義消歧技術為探索詞義消歧的新技術提供了一個新的思路和方法。 -
3 ) semantic classification model based som network we use the classification model to combines attributes within a database. this is done using an unsupervised learning algorithm. the output is used as training data for the next stage
3 ）基於som網路的語義分類模型設計建立som網路模型，將元數據特徵向量進行分類，形成bp網路的目標向量，用於匹配規則的提取。 -
Document clustering techniques have been received more and more attentions as a fundamental and enabling tool for efficient organization, navigation, retrieval, and summarization of huge volumes of text documents. the aim of document clustering is to cluster the documents into different semantic classes in an unsupervised manner
文本聚類作為一種對大規模文本信息進行有效地組織、導航、檢索和概括匯總的關鍵的、基本的技術而日益受到關注，其主要目的是在語義空間里以無監督的方式將文本集中的文本劃分成不同的類。 -
For the data with quantitative attribute, several unsupervised discretization methods of continuous features are discussed. three simple boolean discretization methods ( equal width, equal frequency and cluster ) and fuzzy discretization are simulated based on a real population statistical database
隨后針對數據庫中的模擬量屬性，分析了非監督定量屬性離散化的幾種方法，在一個統計數據庫基礎上模擬研究了分別基於等寬、等頻和聚類的布爾型分段離散化方法和模糊離散化方法。 -
This approach is an unsupervised, statistical, data - driven approach
這種演算法是一種無監督的、基於統計的、數據驅動的方法。
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