parameter sweep machine learning

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use

Machine Learning 4, machine learning Probability-based classification method: Naive BayesBayesian decision theory Naive Bayes is a part of Bayesian decision-making theory. Therefore, before explaining Naive Bayes, let's take a quick look at Bayesian decision-making theory knowledge. The core idea of Bayesian decision-m

p.s. SVM is more complex, the code is not studied clearly, further learning other knowledge after the supplement. The following is only the core of the knowledge, from the "machine learning Combat" learning summary. Advantages:The generalization error rate is low, the calculation cost is small, the result is easy to ex

extension, decision tree, neural network, SVM of support vector machine, rule learning, etc.If it is a regression problem, it can be considered as a continuous form of classification by means of variants or extensions of the above model.If the probability is involved, we can refer to the neural network, Bayesian, maximum likelihood, EM, probability graph, hidden Markov model, reinforcement

(Np.float) # This are important from sklearn.preprocessing import standardscaler scaler = Standardscaler () X = Scaler.fit_transform (X) print "Feature space holds%d observations and%d features"% X.sha PE print "Unique target labels:", Np.unique (y) Many predictive variables care about the relative size of different features, even if these scales may be arbitrary. For example: The basketball team scored more points in each game than they were in a few orders of magnitude. But that does not

Reprint Please specify source: http://www.cnblogs.com/ymingjingr/p/4271742.htmlDirectory machine Learning Cornerstone Note When you can use machine learning (1) Machine learning Cornerstone Note 2--When you can use

better (3)Machine learning Cornerstone Note 16--Machine How to learn better (4)XV, ValidationVerify.15.1 Model Selection problemModel selection issues.So far, many algorithmic models have been learned, but a model requires a lot of parameter selection, which is the focus of this chapter's discussion.Taking the two-yua

Cross entropy cost function 1. Cross-entropy theory Cross entropy is relative to entropy, as covariance and variance. Entropy examines the expectation of a single information (distribution): H (p) =−∑I=1NP (xi) Logp (xi) Cross-Entropy examines the expectations of two of information (distributions):H (P,Q) =−∑I=1NP (xi) logq (xi)For details, please see Wiki Cross entropy y = Tf.placeholder (Dtype=tf.float32, Shape=[none, ten]) ... Scores = Tf.matmul (H, W) + b probs = Tf.nn.softmax (scores) l

1. Vector Norm Norm, Norm, is a concept similar to "Length" in mathematics, which is actually a kind of function.The regularization (regularization) and sparse coding (Sparse coding) in machine learning are very interesting applications.For Vector a∈rn A\in r^n, its LP norm is | | a| | p= (∑IN|AI|P) 1p (1) | | a| | _p= (\sum_i^n |a_i|^p) ^{\frac 1 p} \tag 1Commonly used are: L0 NormThe number of elements i

the concepts of "multilevel", "adaptive" and "average" to simplify the research ideas and ideas behind the numerous and colorful machine learning models and computational methods. Hopefully, this will inspire you to understand some of the models, methods, and future research that machine learning already has.1. Multil

is all 0. And because it can be deduced that b=1nz∗zt=wt∗ (1NX∗XT) w=wt∗c∗w, this expression actually means that the function of the linear transformation matrix W in the PCA algorithm is to diagonalization the original covariance matrix C. Because diagonalization in linear algebra is obtained by solving eigenvalue and corresponding eigenvector, the process of PCA algorithm can be introduced (the process is mainly excerpted from Zhou Zhihua's "machine