機器學習 的英文怎麼說
中文拼音 [jīqìxuéxí]
機器學習
英文
machine learning-
Finally, all the above theoretical results are applied to the analysis of mellituria ii and monks problems. the conclusion is encouraging after the comparison with home precious machine learning algorithms including id family and aq family
最後將本文的理論結果應用於糖尿病病因分析和monks問題，並且把rs方法與傳統的機器學習演算法id家族和aq家族從理論上和實驗上進行了比較，在準確度和規則簡潔度等方面得到了令人鼓舞的結果。Immune evolutionary algorithms are optimal algorithms in essence, therefore, they can be used in some fields, such as cybernation, pattern recognition, optimal design, meshing learning, network security, etc. there are also some examples of these attempts described in this paper
免疫進化計算在本質上講是一種優化演算法，所以它們可以應用於一些諸如自動控制、故障診斷、模式分類、圖象識別、優化設計、機器學習和網路安全性等廣泛領域，本文在這方面也做了一些初步的嘗試。So we draw the conclusion that the new method can reduce the uncertainty and illegibility of tf idf method in many aspects without more work
山東帥范大學礬上畢業論文知識求精是知識獲取必不可少的步驟，機器學習是使計算機具有智能的有效手段， 0 。As for the representation of the individuated pattern - base, we introduces a new classification representation method based on multi - users and multi - topics, so as to make each profile only denote one user ' s one topic. this method makes it possible to express explicitly the user ' s interest. as for the creation of the individuated pattern - base, we adopt the hopfield neural network model, which has the function of ample association and remembrance and may be used to associate with the user ' s interest to create the initial individuated pattern - base
對于個性化模式庫的表示，本文給出了一種多用戶多主題的分類表示方式，使得每個profile文件只表達一個用戶的一個主題，可以更清晰的表達用戶的興趣；對于個性化模式庫的建立，本文採用了機器學習中的hopfield神經網路模型， hopfield網路具有豐富的聯想記憶功能，可以用來對用戶興趣進行聯想，建立用戶的初始個性化模式庫；對于個性化模式庫的維護，採用了基於用戶反饋的學習方法。Mahout ' s goal is to build scalable, apache licensed machine learning libraries
目標建立一個可擴展，遵從阿帕奇協議的機器學習庫。13 selfridge o g. pandemonium : a paradigm for learning. in proc. the symp
作為人工智慧的一個重要分支，機器學習是利用機器模仿人類的智能行為。The support vector approach learns a parsimonious regression model from the given data to avoid the data over - fitting problem
支援向量?歸方法可以在給定的資料中產生一個簡潔的?歸模式，以避免傳統機器學習法中的資料過度學習問題。During the procedure of system design and implementation, the author has made some innovative efforts such as : ( d establishing the user interest orientated model, the model receiving user interests continuously and conjecturing user interests by interaction with the user, accumulating user preferences in information demand, thereby achieving self - adaptive retrieval, ? roviding a feedback method which is based on the human - machine interaction, summarizing the user operations on the interface of result presentation, and designing an algorithm for capturing user operation behaviors, by which the changes in user interests and preferences can be learned potentially, ? ffering a method for user interest mining which can extract subjects of information confirmed by user, thereby conjecturing or predicting different kinds of expressions of the same interest or extracting the new interests or unexpressed interests, ? roposing a solution of personalized internet information retrieval based on the user interests in accordance with the above - mentioned work, the solution having very strong feasibility and practicality with taking user interest model as center, employing machine learning ( active learning and passive learning ) and data mining as tools, and being assisted with network robot,
Piirs系統分析與設計過程中所做的創新性的嘗試主要有以下幾個方面：實現了基於用戶興趣的用戶模型，該模型通過與用戶的交互（主動交互和被動交互） ，不斷地接收用戶的興趣和推測用戶的興趣，積累用戶信息需求的偏好，實現自適應的檢索；提供了一種基於人機交互的反饋方法，對用戶在結果呈現界面上的操作進行了歸納總結，設計了用戶操作捕獲演算法， 「隱性地」學習用戶興趣和偏好的變化；提供了一種用戶需求挖掘的方法，對用戶已確定的信息做進一步的主題挖掘，由此推測或預測用戶同一興趣的不同表述方式或者挖掘出用戶新的或未表達出來的興趣；在上述工作基礎上提出了一套完整的基於用戶興趣的個性化網路信息檢索的解決方案，該方案以用戶興趣模型為中心，以機器學習（主動學習和被動學習）和數據挖掘為手段，輔以網路機器人，具有很強的可行性和實用性。13 milo t, zohar s. using schema matching to simplify heterogeneous data translation. in proc. the 24th int
這一層即是調解器層，它利用一個規則集合，結合機器學習來匹配和集成模式中的要素。One class classification is a machine learning approach different from the traditional pattern recognition approach where two or more class samples are required. however in some real - life cases, we can hardly, even not, get the samples of some classes, or have to pay costly price to obtain the so - needed samples, such as in the case of machinery malfunction. and while in other cases, the sizes of samples among classes are imbalance, such as medical diagnosis
單類分類器是不同於傳統模式識別的一種機器學習方法，傳統模式識別方法一般需要多個類別的樣本（至少兩個） ，而在有些場合中，幾乎無法獲取多類的樣本，或者獲取其樣本所需花費的代價非常高，比如：機器故障中我們不可能為了去獲得故障樣本而讓機器特意產生故障；又有些場合的類別樣本個數嚴重不平衡，比如醫學上的疾病特徵與非疾病特徵的比例是嚴重不平衡的。By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built, svm presents a lot of advantages for resolving the small samples, nonlinear and high dimensional pattern recognition, as well as other machine - learning problems such as function fitting
Svm的基本思想是通過非線性變換將輸入空間變換到一個高維空間，然後在這個新的空間中求取最優分類超平面。它在解決小樣本、非線性及高維模式識別問題中表現出許多特有的優勢，並能夠推廣應用到函數擬合等其他機器學習問題中。Most knowledge discovery or data mining tools and techniques are based on statistics, machine learning, pattern recognition or artificial neural networks
大多數的知識發現或數據挖掘工具和技術是基於傳統的統計、機器學習、模式識別或人工神經網路。Classification is to predict the class label of unknown data with supervisor obtained from experiential data, which is a basic problem in pattern recognitionx machine learning and statistics, as well as in data mining
分類即通過由經驗數據訓練得到的分類器預測未知數據的歸屬，是模式識別、機器學習、統計分析等領域的一個基本問題，也是一種最常見的數據挖掘任務。A double - bagging machine - learning algorithm was used to train classification rules on the basis of a combination of fdt scores and nerve fiber related visual field losses in swap
在聯合fdt評分和swap神經纖維相關的視野缺損基礎上用雙相機器學習系統排列分類法則。Which refers to the theory, methods and applications of using ec, combining with decision making analysis ( dma ), to solve idm problems
進化決策定義為：結合作為機器學習方法的進化計算和傳統決策分析技術，求解智能決策問題的理論、方法和應用的總稱。Data mining merges many important research fields including machine learning, artificial intelligent, statistics, knowledge - base systems and data visualization, etc. however, current algorithms proposed for date mining of association rules require several passes over the analyzed database
挖掘重要數據仍然需要配合許多其他領域的技術才能得到完善有效的結果，其中包括機器學習，人工智慧，統計學原理，數據庫系統，數據可視化等。2 ， a machine learning method for extracting go joseki for computer go system is proposed in this dissertation
2 ，通過機器學習的方法，從棋譜中自動獲取定式，建立定式庫。Do you want to find out how statistics and machine learning can save you time and effort mining text
你想知道統計學和機器學習在挖掘文本方面能夠讓你省時省力的原因嗎？Genetic algorithm ( ga ) is a set of new - global - optimistic search algorithm repeatedly which simulate the process of creature evolution that of darwinian ' s genetic selection and natural elimination
遺傳演算法是模擬達爾文的遺傳選擇和自然淘汰的生物進化過程的一種新的迭代的全局優化搜索演算法，已經廣泛地應用到組合優化問題求解、自適應控制、規劃設計、機器學習和人工生命等領域。The third stage was from mid - 1970 to the beginning of 1980 ' s, in which machine learning went into its flourishing time
第三階段是20世紀70年代中期到80年代初。該階段是機器學習蓬勃發展的階段。分享友人