machine learning algorithms ppt

Analysis of malware through machine learning: Basic Principles of clustering algorithms in Deepviz Since last year, we have discovered that many audiovisual companies have begun to engage in machine learning and artificial intelligence, hoping to find a fast and effective wa

classification method is used to solve the nonlinear problem in two steps, first using a transform to map the data of the original space to the new space, and then using the line-line classification learning method in the new space.Learn the classification model from the training data.If a kernel function is semi-positive, it is valid.In order to solve the problem of outliers, penalties are introduced. The new model should not only make the interval

value;If it becomes smaller, the new puzzle will replace the original;If it becomes larger, the probability of replacing the old one with the new one depends on the current temperature value, where the temperature will begin to slow down at a relatively high value, which is why the algorithm is more receptive to relatively poor performance in the early stages of execution, so that we can effectively avoid the possibility of falling into the local minimum, when the temperature reaches 0, The alg

Perception Machine: This is the simplest machine learning algorithm, but there are a few points to note. The first is the selection of the loss function, and in order to minimize the loss function, the gradient descent method used in the iterative process, finally obtains the optimal w,bThe visual interpretation is to adjust the value of the w,b, so that the sepa

threshold of the class, and it is saved for clustering. This method of finding EPs mainly takes into account that data sets of different densities should be based on the density of each data. The appropriate thresholds were selected for clustering. Because the parameters used in clustering can only determine the density difference in the same class of data in the cluster results, the error caused by the parameter selection will not have a great effect on the clustering result.2.2 DBSCAN cluster

. Or after the derivation of the formula can not be interpreted, or the number of unknown parameters is greater than the number of equations. At this point, the iterative algorithm is used to find the optimal solution step-after-step. In particular, if the optimization function is a convex function, then there is a global optimal solution, if the function is non-convex, then there will be many local optimal solutions, so the importance of convex optimization is self-evident. People always wan

]) *double (Dy[i])#Sqx = double (Dx[i]) **2Sumxy= VDOT (Dx,dy)#returns the point multiplication of two vectors multiplySQX = SUM (Power (dx,2))#Square of the vector: (x-meanx) ^2#calculate slope and interceptA = sumxy/SQXB= meany-a*MeanxPrintA, b#Draw a graphicPlotscatter (XMAT,YMAT,A,B,PLT)7.1.4 Normal Equation Group methodCode implementation of 7.1.5 normal equation set#data Matrix, category labelsXarr,yarr = Loaddataset ("Regdataset.txt")#Importing Data Filesm= Len (Xarr)#generate x-coordinat

value of 3.For example: Np.random.randint (3, 6, size=[2,3]) returns data with a dimension of 2x3. The value range is [3,6].(4). Random_integers (low[, high, size]), similar to the above randint, the difference between the range of values is closed interval [low, high].(5). Random_sample ([size]), returns the random floating-point number in the half-open interval [0.0, 1.0]. If it is another interval [a, b), it can be converted (b-a) * Random_sample ([size]) + AFor example: (5-2) *np.random.ran

specific job requirements, image algorithm For example, now deep learning hot not I said, so the basic convolution neural network algorithm , image classification , image detection The more famous paper in recent years should read it. If you have a condition, use it like a caffe,tensorflow frame.2. Machine Learning EngineerThis post is basically the same as the

: KneighborsclassifierCommon: KNEIGHBORSCLASSIFIER:KNN nearest neighbor algorithm, nearestneighbors: Nearest neighbor algorithm, kneighborsregressor:k nearest neighbor algorithm, nearestcentroid: Nearest centroid algorithm4. Logistic regression algorithm: logisticregression5. Stochastic forest algorithm, random Forest Classifier:randomforestclassfierOne of the most commonly used: Randomforestclassifier: Random forest algorithm, baggingcclassifier:bagging bagging algorithm6. Decision Tree algorit

can be processed.Cons: Easy to fit.How to avoid overfitting:(1) dimensionality reduction, can use PCA algorithm to reduce the dimension of the sample, so that the number of theta of the model is reduced, the number of times will be reduced, to avoid overfitting;(2) regularization, the design of regular items regularization term.The regularization function is to prevent some properties before the coefficient weight is too large, there has been a fitting.Note that the way to resolve overfitting i

Supervised learningKNN k Nearest Neighbor algorithmdef classify0 (InX, DataSet, labels, k):Datasetsize = dataset.shape[0]Diffmat = Tile (InX, (datasetsize,1))-DataSetSqdiffmat = diffmat**2Sqdistances = Sqdiffmat.sum (Axis=1)distances = sqdistances**0.5classcount={}For I in range (k):Voteilabel = Labels[sorteddistindicies[i]]Classcount[voteilabel] = Classcount.get (voteilabel,0) + 1Sortedclasscount = sorted (Classcount.iteritems (), Key=operator.itemgetter (1), reverse=true)return sortedclasscoun

training samples.The above two or three can be done in the case of inverse existence, but what if the characteristics of the data are more than the sample points, because the inverse is not present at this time? You can use the ridge regression method to solve this problem, that is, it will be converted to, the other and the previous approach is similar.Of course, there is a method called forward stepwise regression, it is through each step to a certain weight increase or decrease a small value

paper is usually European-style distance, Pearson coefficient or cosine similarity.Assuming that a matrix A is established, the M*n matrix, the rows are all users, n is all items, each element of the matrix represents the user's rating of the item, then the item-based or user-based recommendation is to calculate the similarity of all columns or all rows. In real life, this matrix is very sparse.Topic: Recommend users to buy TOPN itemsThe Matrix C is a m*n matrix, each row represents each user,

One of the top ten algorithms for Machine Learning: EM algorithm. One of the top 10, which makes people think Nb-rich. What is Nb? We generally say someone is Nb because he can solve problems that others cannot solve. Why God is God, because God can do things that many people cannot do. So what problems can the EM algorithm solve? Or the reason why the EM algorit

The idea of clustering: dividing a DataSet into several subsets (called a cluster cluster) that you don't want to cross, each potentially corresponding to a concept. But the practical significance of each cluster is determined by the users themselves, and the clustering algorithm will only be divided.The role of Clustering:1) can be used as a separate process for finding a distribution pattern of data2) as a preprocessing process for classification. First, classify data is clustered and then the

Naive Bayesian algorithm is to look for a great posteriori hypothesis (MAP), which is the maximum posteriori probability of the candidate hypothesis.As follows:In Naive Bayes classifiers, it is assumed that the sample features are independent from one another:Calculate the posterior probability of each hypothesis and choose the maximum probability, and the corresponding category is the result of the sample classification.Advantages and DisadvantagesVery good for small-scale data, suitable for mu

In machine learning, there are many problems, there is no analytic form of solution, or analytic form of the solution but the computation is very large (for example, the problem of the least-squares solution), for such problems, we usually choose to use an iterative optimization method to solve.These commonly used optimization algorithms include gradient descent

SAS graphical user interfaces help you build machine-learning models and implement an iterative machine learning process. You don ' t have a advanced statistician. Our comprehensive selection of the machine learning

Nine algorithms for machine learning---naive Bayesian classifierTo understand the Naive Bayes classificationBayesian classification is a generic term for a class of classification algorithms, which are based on Bayesian theorem, so collectively referred to as Bayesian classification. Naive naive Bayesian classification