監督學習 的英文怎麼說
中文拼音 [jiāndūxuéxí]
監督學習
英文
supervised learning-
Any software that calls and uses the ole db api
聚類分析是一種非監督學習技術。Supervised learning of heuristic function for refutation
反演啟發函數的監督學習演算法Synchronal generator excitation controller based on snc neural network
同步發電機監督學習神經網路勵磁控制器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。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。The main work includes : the research and conments about some recognition methods ; the research and comments about three kind of mathematics morphologic arithmetic ; clustering ; matlab embedded in the vb ; the difference analysis in the dynamic image and so on
主要做的工作包括幾種識別方法的研究與評述；三種數學形態學演算法的實現以及各自的優劣比較；利用無監督學習進行聚類以及matlab在圖像處理中的嵌入；動態圖像的差分分析等。In the respect of neural networks control for non - linear and uncertain system, a review of some available control strategy is made. combining neural networks control and conventional control strategy supervised learning, no supervised learning and reinforcement learning neural networks self - studying and adaptive control systems for ship course control are proposed. the thesis studies particularly their characteristics
在非線性和不確定性系統的神經網路控制方面，論文總結了一些現有的神經網路自學習控制系統，然後將神經網路和常規控制（例如pid控制、自適應控制、內模控制等）結合起來，根據船舶操縱的特點，詳細研究和分析了有監督學習、無監督學習和再勵學習的船舶航向神經網路自學習型自適應控制系統。In recent years, reinforcement learning has become one of the key research areas in artificial intelligence and machine learning and it has attracted many researchers in other fields including operations research, control theory and robotics. reinforcement learning is different from supervised learning in that no teacher signals are needed and a reinforcement learning system learns by interacting with the environment to maximize the evaluative feedback from the environment
增強學習與監督學習的不同之處在於，增強學習不要求給定各種狀態下的期望輸出即教師信號，而強調在與環境交互中的學習，以極大（或極小）化從環境獲得的評價性反饋信號為學習目標。9 blum a, kalai a. a note on learning from multiple - instance examples. machine learning, 1998, 30 : 23 - 29. 10 maron o, lozano - p erez t. a framework for multiple - instance learning
本文分析了若干具有代表性的多示例學習演算法，揭示出監督學習方法可以轉化為多示例學習方法，只需將學習方法的注意焦點從對示例的區分轉變到對包的區分。It overcomes the limitation in the assumption in other semi - supervised learning algorithms that probabilistic distribution of data is known, and has the strong ability of learning new patterns and correcting errors because of stability and plasticity of the adaptive resonance theory
在該系統中取消了一般半監督學習演算法中假定已知數據概率分佈的條件限制，利用自適應諧振理論的穩定性和可塑性，使其具有非常強的學習新模式和糾正錯誤能力。Finally, most supervised learning neural networks train themselves through minimizing mean squared error. but when the neural network models trained in this way are used to do forecasting, the existence of outliers result in great imprecision
最後，大多數監督學習神經網路是通過最小化訓練集的均方差來訓練網路，而野值的存在導致這種訓練的神經網路模型在預測時會產生極大的不精確性。Secondly, aiming at the necessity of pixel - based tongue color classification system, this dissertation proposes a 2 - stage fcm algorithm. it applies the techniques of pattern classification and makes the better automation of tongue color model construction true
其次，針對基於像素的舌顏色分類系統的需要，本文提出了一種「二次fcm演算法」 ，採用半監督學習的方式，較為自動化地解決了舌色苔色分佈模型的建立問題。This dissertation thus aims at helping the development of computerized tongue diagnosis and researching on the methodology of color training and classification of tongue images. the main contributions of this dissertation include : designing the framework of pixel - based tongue color classification system ; proposing the 2 - stage fcm algorithm and solving the tongue color model construction problem in pixel - based tongue color classification system ; proposing the dynamically local knn algorithm for tongue substance and tongue coating color classification, and improving the system speed greatly ; proposing the 12 - dimension feature vector of color ratio and applying it to color classification of tongue image ; doing research on the automatic diagnosis of diseases and symptoms using color, texture and shape information
本文的主要貢獻在於:設計了基於像素的舌顏色分類系統結構;提出了半監督學習方式的「二次fcm演算法」 ，解決了基於像素的舌顏色分類系統的舌色苔色分佈模型的建立問題;提出了「動態局部knn演算法」並將其應用於舌色苔色分類中，解決了舌色苔色分類的速度問題;提出了舌圖像的「 12維顏色比例特徵向量」 ，並應用其實現了對舌圖像的顏色分類;採用顏色、紋理和舌形的信息融合方式，對疾病和證候的自動診斷進行了研究。A semi - supervised learning system was proposed based on art ( adaptive resonance theory )
摘要根據自適應諧振理論提出了半監督學習自適應諧振理論系統。It also proposes a method of supervised learning to train the decision function and provides the corresponding method of calculation to realize it
提出了一種通過監督學習來訓練判別函數的方法，並給出了相應的實現演算法。Journal of the royal statistics society - b, 1977, 39 : 1 - 38. 17 zhang q, goldman s a. em - dd : an improved multi - instance learning technique
集成學習利用多個個體學習器來解決同一個問題，以往研究表明，集成學習技術能夠顯著提高監督學習器的泛化能力。分享友人