supervised learning 中文意思是什麼
supervised learning
解釋
監督學習- supervised : 有監督的
- 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...
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Supervised learning of heuristic function for refutation
反演啟發函數的監督學習演算法 -
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。 -
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。 -
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。 -
J. xiao, j. su, g. zhou, and c. tan, “ protein - protein interaction extraction : a supervised learning approach ”, first international symposium on semantic mining in biomedicine ( smbm ), vol. 148, 2005
蔣明村， 「使用自動化樣板建立的蛋白質交互作用驗證系統」 ，國立成功大學資訊工程學系碩士論文，未出版， 2007 。 -
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。 -
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。 -
According to the utilized face database, three facial expression categories are defined : neutral, happiness and anger. the categorization architecture is based on a som. in order to eliminate influence of initial values and sequence of input examples in som, supervised learning is introduced into the training stage
分類器的設計採用的是基於自組織神經網路的方法，為了克服傳統的自組織映射神經網路的訓練結果容易受訓練樣本的輸入順序和權值初值影響，而導致訓練結果不符合期望的問題，因此，在訓練過程中引入了監督機制，以使訓練結果與期望相符。 -
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。 -
As we all know, the methods of feature selection for supervised learning perform pretty well with strong practice and simple operation
眾所周知，在有指導學習環境下，出現了很多性能優越、實用性強和操作方便的屬性選擇方法。 -
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。 -
The typical ones include relief - f, information gain and chi - square etc. feature selection was considered as feature selection in supervised learning from traditional view
其中的典型代表有relief - f 、信息增益和卡方檢驗等。過去傳統意義上的屬性選擇通常是指在有指導學習環境下的屬性選擇。 -
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。 -
Classification is a sort of supervised learning ( i. e., the learning of the model is " supervised " in that it is told to which class each training sample belongs )
需要指出的是：分類是一種有指導的學習（即模型的學習在被告知每個訓練樣本屬于哪個類的「指導」下進行） 。 -
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 -
Supervised learning with the use of regression and classification networks with sparse data sets will be explored
也將在課程中以帶有稀疏值理論的分類神經網路與回歸的使用來探討監督式學習。 -
Experiments show that it can acquire lexical items with high frequency effectively and efficiently without the support of the dictionary and the supervised learning in term of corpus
實驗表明：在無需詞典支持和利用語料庫學習的前提下，該演算法能夠快速、準確地抽取中文文檔中的中、高頻詞條。
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