Classification:?Classification aims to divide the items into categories. We have a binary classification and multi-class classification. We need the correct labelled training data to classify the new test samples.
Pattern Recognition:?Goal of Pattern Recognition is the Classification of data or to be in more general Patterns into classes or categories. In Pattern Recognition there is no need to have a labelled training data to classify.
PR generally categorized according to the type of learning procedure used to generate the output value.
1. Supervised Learning(set of training data has to be provided which is labelled with correct output)?(classification)
The model underlying categories are perfectly known in terms of probability density function(pdf) and categories label.
The model is known (e.g. suppose normal density with mean and covariance matrix), but not so some of its parameters
Not even the model is known: there is no prior parameterized knowledge about the form of the underlying probability structure and all the information for classification will come from the training samples alone
Classification Analysis: Classification pertains to the known number of groups and the objective is to assign new data points to one of these groups
2. Unsupervised Learning?(training data is not labelled i.e is any training data)(Clustering)
Not even the labels of input patterns are known and our classifier needs to determine the cluster structure
Cluster Analysis: Cluster analysis can be used to partition a large set of data into groups, called clusters, so that the data points in a group are similar to each another, while those in distinct groups are not similar to those in the other groups
PR?does not absolutely mean that you have to finally classify it to a certain class. Clustering is one such typical example. Consider there are 100 samples and you perform clustering on them, i.e., you just form groups of similar objects based on some similarity measure. This is a form of Pattern Recognition.
Pattern Classification:?For example say a new test data is obtained and the pattern of the test data is identified with a group of certain training samples or a cluster of similar samples. Thereafter, the moment the new test sample is assigned a class label, it will be called Pattern Classification.
References:
Pattern recognition Clustering Classification
分類：分類旨在將項(xiàng)目分為幾類。我們有二元分類和多類分類竖配。我們需要正確的標(biāo)記訓(xùn)練數(shù)據(jù)來(lái)對(duì)新測(cè)試樣本進(jìn)行分類蔗蹋。
模式識(shí)別：模式識(shí)別的目標(biāo)是數(shù)據(jù)的分類或更廣泛的模式分類或類別树碱。在模式識(shí)別中吴叶，不需要標(biāo)記的訓(xùn)練數(shù)據(jù)來(lái)進(jìn)行分類开呐。
PR通常根據(jù)用於生成輸出值的學(xué)習(xí)過(guò)程的類型進(jìn)行分類杜耙。
1.監(jiān)督學(xué)習(xí)（必須提供一套標(biāo)有正確輸出的訓(xùn)練數(shù)據(jù)）（分類）
基於概率密度函數(shù)（pdf）和類別標(biāo)籤搜骡，模型基礎(chǔ)類別是完全已知的。
該模型是已知的（例如佑女，假設(shè)具有均值和協(xié)方差矩陣的正常密度）记靡，但不是那麼一些參數(shù)
甚至模型都不知道：沒(méi)有關(guān)於基礎(chǔ)概率結(jié)構(gòu)形式的先驗(yàn)參數(shù)化知識(shí)，所有分類信息都來(lái)自單獨(dú)的訓(xùn)練樣本
分類分析：分類與已知的組數(shù)有關(guān)团驱，目標(biāo)是將新數(shù)據(jù)點(diǎn)分配給其中一組
2.無(wú)監(jiān)督學(xué)習(xí)（訓(xùn)練數(shù)據(jù)未標(biāo)記摸吠，即任何訓(xùn)練數(shù)據(jù)）（聚類）
甚至輸入模式的標(biāo)籤都不知道，我們的分類器需要確定集群結(jié)構(gòu)
聚類分析：聚類分析可用於將大量數(shù)據(jù)分組為稱為群集的組嚎花，以便組中的數(shù)據(jù)點(diǎn)彼此相似寸痢，而不同組中的數(shù)據(jù)點(diǎn)與其他組中的數(shù)據(jù)點(diǎn)不相似
PR並不絕對(duì)意味著你必須最終將它歸類到某個(gè)類。聚類就是一個(gè)典型的例子紊选√渲梗考慮有100個(gè)樣本並對(duì)它們執(zhí)行聚類，即兵罢，您只需根據(jù)某些相似性度量形成相似對(duì)象組献烦。這是模式識(shí)別的一種形式。
模式分類：例如卖词，獲得新的測(cè)試數(shù)據(jù)巩那，並且使用一組特定訓(xùn)練樣本或一組類似樣本來(lái)識(shí)別測(cè)試數(shù)據(jù)的模式。此後此蜈，在為新測(cè)試樣本分配類標(biāo)籤的那一刻即横，它將被稱為模式分類。
模式識(shí)別主要是對(duì)已知數(shù)據(jù)樣本的特征發(fā)現(xiàn)和提取舶替，比如人臉識(shí)別令境、雷達(dá)信號(hào)識(shí)別等，強(qiáng)調(diào)從原始信息中提取有價(jià)值的特征顾瞪，在機(jī)器學(xué)習(xí)里面舔庶，好的特征所帶來(lái)的貢獻(xiàn)有時(shí)候遠(yuǎn)遠(yuǎn)大于算法本身的貢獻(xiàn)；
模式分類可以理解為對(duì)具有了給定特征的樣本通過(guò)分類器來(lái)進(jìn)行分類陈醒，典型的模式分類方法有線性分類器（感知器惕橙，F(xiàn)isher判別）、非線性分類器（BP神經(jīng)網(wǎng)絡(luò)钉跷、RBF弥鹦、SVM），現(xiàn)實(shí)場(chǎng)景中主要是非線性啦，還有貝葉斯判決彬坏、C4.5朦促、隨機(jī)森林等等等等。
這兩者還會(huì)有個(gè)區(qū)別栓始，目前模式識(shí)別主要是無(wú)監(jiān)督學(xué)習(xí)务冕，人為構(gòu)造算法的成分比較大（比如，人臉里面幻赚，工程師會(huì)事先告訴算法某些地方的特征）禀忆，而在模式分類上，機(jī)器學(xué)習(xí)可以發(fā)揮的空間就比較大落恼，只要有了訓(xùn)練樣本箩退，適當(dāng)降維和清洗數(shù)據(jù)，分類器是可以自動(dòng)發(fā)現(xiàn)樣本中的特征的佳谦，此所謂有監(jiān)督機(jī)器學(xué)習(xí)戴涝。
