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What are two models?
We have come to these two concepts from a few words:1, machine learning is divided into supervised machine learning and unsupervised machine learning;2, supervised machine

Today I saw in this article how to choose the model, feel very good, write here alone.More machine learning combat can read this article: http://www.cnblogs.com/charlesblc/p/6159187.htmlIn addition to the difference between machine learning and data mining,Refer to this arti

Time: 2014.06.26
Location: Base
Bytes --------------------------------------------------------------------------------------I. Training error and test error
The purpose of machine learning or statistical learning is to make the learned model better able to predict not only known data but also unknown data. Different

R Language ︱ machine Learning Model Evaluation Index + (TURN) Model error four reasons and how to correctThe author's message: the way of cross-validation in machine learning is the main model

model: Typically, you learn a model that represents the target, and then use it to search the image area and then minimize the refactoring error. Similar to the build model describes a goal, then the pattern matching, in the image to find the best match with the model of the region, is the target.Discriminant

Label: style blog HTTP Io ar use for SP strong
I. Introduction
This document is based on Andrew Ng's machine learning course http://cs229.stanford.edu.
In the previous supervised learning regression model, we used the training set to directly model the conditional prob

decision trees (decision tree) 4
Cited examplesThe existing training set is as follows, please train a decision tree model to predict the future watermelon's merits and demerits.Back to Catalog
What are decision trees (decision tree) 5
Cited examplesThe existing training set is as follows, please train a decision tree model to predict the future watermelon's merits

Anyone who knows a little bit about supervised machine learning will know that we first train the training model, then test the model effect on the test set, and finally deploy the algorithm on the unknown data set. However, our goal is to hope that the algorithm has a good

these matrices, and the θ superscript (j) becomes a wave matrix that controls the action from the first layer to the second or second to the third layer. The first hidden unit calculates its value in this way: A (2) 1 equals the S function or S-excitation function, also called the logical excitation function, which acts on the linear combination of this input. The second hidden unit equals the value of the S function on this linear combination. The parameter matrix controls the mapping from thr

Perception Machine (Perceptron)The Perceptron (Perceptron) was proposed by Rosenblatt in 1957 and is the basis of neural networks and support vector machines. Perceptron is a linear classification model of class Two classification, its input is the characteristic vector of the instance, the output is the class of the instance, and the value of +1 and 12 is taken. The perceptual

One model evaluation For these two errors, the test error can reflect the learning method to the unknown test data set prediction ability, is an important concept in learning, usually the learning method to the unknown data prediction ability is called generalization ability (generalization ability). Two generalizat

1. Training error: The error of the learner in the training set, also known as "experience Error"2. Generalization error: The error of the learner on the new sampleObviously, our goal is to get a better learner on a new sample, which is a small generalization error.3. Overfitting: The learner learns the training sample too well, leading to a decline in generalization performance (learning too much ...). Let me think of some people bookworm, reading de

-validation approach. Cross-validation
A simple idea to solve the above model selection problem is that I use 70% of the data to train each model, with 30% of the data for training error calculation, and then we compare the training errors of each model, we can choose the training error is relatively small model. If yo

change then the iteration can stop or return to ② to continue the loopExample of using the K-mans algorithm on handwritten digital image dataImportNumPy as NPImportMatplotlib.pyplot as PltImportPandas as PD fromSklearn.clusterImportKmeans#use Panda to read training datasets and test data setsDigits_train = Pd.read_csv ('Https://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tra', hea

Today, Google's robot Alphago won the second game against Li Shishi, and I also entered the stage of the probability map model learning module. Machine learning fascinating and daunting.--Preface1. Learning based on PGMThe topological structure of Ann Networks is often simil

Developing a complex depth learning model using Keras + TensorFlow
This post was last edited by Oner at 2017-5-25 19:37Question guide: 1. Why Choose Keras. 2. How to install Keras and TensorFlow as the back end. 3. What is the Keras sequence model? 4. How to use the Keras to save and resume the pre-training model. 5. H

) ^2\)To break it apart, it was \ (\frac1 2 \bar{x}\) where \ (\bar{x}\) is the mean of the squares of $h _θ (x_i)? Y_i $, or the difference between the predicted value and the actual value.This function is otherwise called the "Squared error function", or "Mean squared error". The mean is halved \ ((\frac1 2) \) as a convenience for the computation of the gradient descent, as the derivative Term of the square function would cancel out the \frac1 2\ . The following image summarizes what is the c

The upcoming Apache Spark 2.0 will provide a machine learning model persistence capability. The persistence of machine learning models (the preservation and loading of machine learning

obtained for all possible combinations x,u. Complete data is the complete probability, and incomplete data is the probability of its marginal missing variable. In M-step, the system parameter theta is updated with sufficient statistics.For example, in the Bayesian classifier, we only have data and no class value for the data. (It really can be lost .....) At this point, if the EM algorithm is used, the Bayesian classifier changes from supervised learning

Summary:Classification and Regression tree (CART) is an important machine learning algorithm that can be used to create a classification tree (classification trees) or to create a regression tree (Regression tree). This paper introduces the principle of cart used for discrete label classification decision and continuous feature regression. The decision tree creation process analyzes the information Chaos Me

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