learning machine 中文意思是什麼
learning machine
解釋
學習機-
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。 -
Knowledge discovery in databases ( kdd ) is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, statistics, neural networks, and pattern recognition
數據庫中的知識發現（ knowledgediscoveryindatabases ， kdd ）是當前涉及人工智慧、數據庫等學科的一門非常活躍的研究領域。 -
Chapter 2 has systematically discussed machine learning problem, which is the basic of svm, with statistical learning theory or slt. secondly, chapter 3 has educed the optimal hyperplane from pattern recognition
第二章探討了支持向量機理論基礎? ?學習問題，尤其是對vapnik等人的統計學習理論（ slt ）結合學習問題作了系統的闡述。 -
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。 -
This article is composed of three parts, data collection, data filtering and machine learning. these three parts were assembled organically and enhanced the intelligence as far as it can at every point to improve on the traditionary word segmentation algorithm and inductive learnings
三個子系統通過知識庫有機的結合在一起，並盡可能地在系統的各個環節利用agent的思想提高智能化，並對傳統的分詞演算法，歸納學習演算法做了融合和改進。 -
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。 -
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。 -
Data mining is an important research subject in the field of information technology. it means a process of nontrivial extraction of implicit, previously unknown and potentially useful information from data in databases or datawarehouses. it involves such subject areas as database, artificial intelligence, machine learning and statistics. classification analysis is an important data mining problem
數據挖掘（ datamining ）是信息處理技術研究領域的一項重要課題。它是指從大型數據庫或數據倉庫中提取隱含的、未知的、非平凡的以及有潛在應用價值的信息或模式的過程。 -
While network - based intrusion detection systems usually detect packet headers or part of those headers, this system examines the packet payload as well as headers. not only the attributes are detected respectively, but also the relationships of attributes occurrence are detected according to a set of conditional rules, which is generated automatically by a machine learning algorithm
常見的基於網路的入侵檢測系統一般只對網路數據包的包頭或包頭中的某些內容進行考察，而本文實現的原型系統不僅考察數據包的包頭，而且還考察數據包應用負載的部分內容。 -
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。 -
2 moghaddam b, pentland a. probabilistic visual learning for object representation. ieee transactions on pattern analysis and machine intelligence, 1997, 19 : 696 - 710. 3 jolliffe i t. principal component analysis
因此，當輸入圖像具有較大差異例如發型胡須眼鏡等的較大變化而對全局特徵造成較大影響時，本徵臉方法往往會失效。 -
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。 -
Aiming at the relativity between repeated or similar samples and characteristic parameters during diagnosis of characteristic data, an effective data analysis approach for characteristic data compression from bi - direction is presented, which can reduce the burden of learning machine without losing the connotative characteristic knowledge of characteristic data
摘要對診斷特徵數據中重復或相似事例樣本和特徵參量之間可能針存在的相關性，提出一種有效的特徵數據雙向壓縮預處理方法，該法在不損失數據隱含的特徵知識的前提下，能有效降低學習機器的學習負擔。 -
( 4 ) support vector machine ( svm ) is a novel powerful learning machine, which can solve small - sample learning problem better. the basic ideas of statistical learning theory ( slt ) and svm are introduced, and the characteristics of svm are illuminated
本文參考前人的工作，對統計學習理論和支持向量機的相關知識進行了介紹，分析了svm模型的特點，並對選用不同的模型和參數對支持向量機模型的影響進行了探討。 -
And the support vector machine ( svm ) is a new kind of learning machine, which is based on the statistical learning theory. its complete theory and excellent performance make a potential future for mine intelligence. the aim of this thesis is to realize 3 - class underwater targets " recognition by means of data mining technique
其中的支持向量機技術是在統計學習理論框架下提出的一種新的學習機器，其完備的理論基礎和優良的推廣性能，為水雷兵器引信技術的智能化指引了一個很有發展潛力的方向。 -
Basing on it we bring forward the disambiguation strategy using rule techniques and statistics techniques. in rule model, the acqusition method of rules base is improved. we use the part - of - speech of syntactic category to replace the syntactic category. in addition, statistics method is used to help to construct the rule base. in statistics model, the concept of learning machine - made is presented. in according to the result of learning, the method of calculating transition probabilities and symbol probabilities are amended
在規則方法中，改進了規則庫的構建方法，用兼類詞詞性代替兼類詞本身，並嘗試使用統計輔助構建規則庫；在統計方法中，在二元語法模型基礎上引入了學習機制的概念，根據學習結果對詞性概率和詞匯概率的獲取方法進行了修正。 -
The support vector machine ( svm ) is a novel type of learning machine which has some remarkable characteristics such as good generalization performance, the absence of local minima and fast computing speed
摘要支持向量機（ svm ）是一種新穎的機器學習方法，具有泛化能力強、全局最優和計算速度快等突出優點。
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