unsupervised learning 中文意思是什麼

unsupervised learning 解釋
非監控式學習
  • unsupervised : 無監督的
  • learning : n 學，學習；學問，學識；專門知識。 good at learning 善於學習。 a man of learning 學者。 New learn...
  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 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。
  6. 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。
  7. 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。
  8. 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。
  9. One method was supervised recognition, which was to take advantage of some known information to determine a given sequence whether contained some specific functional elements ; the other way was unsupervised learning, which was to utilize some measures of comparability and some search algorithm to discovery some potential signals in biosequences

    一種是有指導的識別方法，即利用已知的信息判讀一段未知的序列中是否含有某種功能元件;另一種是無指導的學習方法，即利用一些相似性指標，通過搜索演算法發現序列中可能蘊含的信號。
  10. Decision theory, statistical classification, maximum likelihood and bayesian estimation, non - parametric methods, unsupervised learning and clustering

    決策理論，統計分類，最大似然和貝葉斯估計，非參數方法，非監督的學習與聚類。
  11. According to various of applications of the datasets, feature selection algorithms can be categorized as either supervised learning or unsupervised learning feature selection approaches

    屬性選擇問題可以分為有指導學習環境下的選擇和無指導學習環境下的選擇。
  12. The point of paper is to make a deep survey on feature selection for unsupervised learning, which can provide some valuable practical experience of enhancing efficiency of data mining for unsupervised learning

    本文的重點就是對無指導學習下的屬性選擇進行深入研究，以此為無指導學習環境下的提高數據挖掘的效率提供一些實踐經驗。
  13. The distinct difference between supervised learning and unsupervised learning lies in whether the example consists of the pre - processed output value

    這兩種方法最大的區別就在於學習樣本是否包含有預先規定好的輸出值。
  14. 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隱層神經元個數，隱中心矢量，平滑參數等要素對網路分類效果的影響，並用pnn實現了異或邏輯問題;提出了一種新的pnn有監督學習演算法:用學習矢量量化對各類訓練樣本進行聚類，對平滑參數和距離各類模式中心最近的聚類點構造區域，並採用遺傳演算法在構造的區域內訓練網路，實驗表明:該演算法在分類效果上優于其它pnn學習演算法
  15. Based on unsupervised classification, improve the original kononen neural network ( knn ) by modifying learning rate and kernel function. and developed two fusion rules based on the magnitude of providing recognition information of each information sources

    1 ）改進基本的kohonen神經網路（ knn ）學習率和核函數，並針對每個信息源對最終識別貢獻的大小，提出加權與不加權兩種融合規則。
  16. Both theories are combined to classify the documents by unsupervised learning and discuss the method in which new rules, applied to new unclassified documents, can be formed after classifying the training documents

    本文利用文檔聚類和粗糙集約簡相結合的方法，對訓練文檔進行分類，形成規則后對新加入的未分類文檔進行歸類。
  17. However, as data mining has penetrated to more application domains, feature selection for unsupervised learning becomes concerned increasingly. therefore, without any information of classification from samples, method of feature selection cannot result in satisfying effect

    由於無指導學習沒有關于樣本類別的重要信息，在面對大量屬性的數據集所進行屬性選擇的效果不如有指導學習環境下的結果。
  18. If methods use a training data set with correct classifications for learning specific predictive patterns, they are called “ supervised ”. if we just use the data itself to and internal structure, the method is called “ unsupervised ”

    如果用來建立模型的訓練集合中的每個樣本已經有了明確的類別屬性，那麼在這樣的數據集上建立模型的過程就是有指導學習。
  19. Firstly, making a comprehensive survey of feature selection for unsupervised learning in the past research, these are theoretical foundations of my paper. secondly, we introduce a novel methodology ulac ( feature selection for unsupervised learning based on attribute correlation analysis and clustering algorithm )

    在已經深入了解和體會現有發展的基礎上，提出一種新型的屬性選擇模型? ?無指導學習環境下基於屬性相關性分析和聚類演算法的屬性選擇方法ulac （ featureselectionforunsupervisedlearningbasedonattribute - correlationanalysisandclusteringalgorithm ） 。
  20. The other is that when the extending areas of the samples overcross, wrong classification of the samples will occur. as for the first problem a genetic algorithm is used to improve the process of the best parameters " finding. and as for the latter a kind of improved hamming net which uses supervised and unsupervised learning method is employed

    針對模糊hamming網路在應用中存在的參數調整效率低下以及難以保證參數最優的問題，提出了應用遺傳演算法進行參數調整的改進方法；針對該網路在樣本離散范圍發生交疊情況下導致歸類錯誤的問題，研究了對于不同模式採用不同的警戒參數的有監督無監督混合學習的改進演算法。
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