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http://blog.csdn.net/ppn029012/article/details/8908104
Machine Learning---2. From maximum likelihood to view linear regression classification: Mathematics machine Study 2013-05-10 00:34 3672 people read comments (15) Collection Report MLE machine learning
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Prediction problems in machine learning are usually divided into 2 categories: regression and classification .Simply put, regression is a predictive value, and classification is a label that classifies data.This article describes how to use Python for basic data fitting, and how to analyze the error of fitting results.This example uses a 2-time function with a ra
sessions should be conducted before they can be completed?In general, the number of sessions = total size of the sample/out-of-sample data. SizeHow many data should you choose to use as an out-of-sample data?The different requirements have different options, but one rule of thumb is:Out-of-sample data size = Total siz
determine the type of input vector x of the calculation process to specify the naïve Bayesian computation processBy the conditional probability formula get P (y=ck| x=x) = P (y=ck,x=x)/P (x=x) = P (x=x | Y=CK) P (y=ck)/P (x=x)The full probability formula is available (replace P (x=x)): Note: Argmax refers to CK with the largest probability of taking One of the I (..) is the indicator function, of course, these probabilities in the actual can be very block, you can se
ImageNet: non-commercial visualisation of big dataAs of May 1, 2015, the Imagenet database has more than 15 million images. cifar10:10 Types of object recognition data setsData set contains 60,000 images of 32*32, total 10 objects (6,000 images/class)Among them, 50,000 as training images,10,000 as testing imagesmnist : handwritten font recognition data set10 types of d
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Data Set Classification
in machine learning with supervised (supervise), datasets are often divided into two or three groups: the training set (train set) validation set (validation set) test set.
The training set is used to estimate the model, the validation set is used to determine the network structure or the parameters that control the complexity of the mod
, select the most frequently occurring classification of the K most similar data as the classification of the new data.
The movie category KNN analysis (image from the network)
Euclidean distance (Euclidean Distance, Euclidean metric)
Calculation process Diagram
CaseThe code is written in Jupyter notebook.
1 ImportNumPy as NP2 ImportPandas as PD3 fromPa
neural network are in the same form.2, for the RBF network the first level input parameters are fixed: | | x-μi| |, but for neural network, the corresponding parameters need to be learned by reverse propagation.3, for the RBF network when the first level input value is very large, the corresponding node output will become very small (Gaussian model), and for the neural network does not exist this feature, the root of the specific node used by the function. 4. RBF and Kernel methodsThen look at
Normally to perform supervised learning you need both types of data sets:
In one dataset (your ' gold standard ') you had the input data together with correct/expected output, this dataset is usual Ly duly prepared either by humans or by collecting some the data in semi-automated. But it's important, and the expected o
attribute in the data set. The general situation is somewhere between the two.D. High-dimensional mappingMap properties to high-dimensional space. This is the most precise approach, which completely retains all the information and does not add any additional information. For example, Google, Baidu's CTR Prediction model, pre-processing will be all the variables to deal with this, up to hundreds of millions of dimensions. The benefit of this is that t
See original book 2.1-2.2 sectionThe new dataset is like a wrapped gift, filled with promise and hope!But until you open it, it remains mysterious!I. Structure and terminology of the underlying problem, characteristics of the machine learning data setTypically, rows represent instances, columns represent attribute characteristicsproperty, the
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Preface:
This article describes Ng's notes about machine learning about SVM. I have also learned some SVM theories and used libsvm before. However, this time I have learned a lot about Ng's content, and I can vaguely see the process from Logistic model to SVM model.
Basic Content:
When using the linear model for classification, You can regard the parameter vector as a variable. If the cost function
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