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English Choose a language for shopping. Length: pages. Format: Print Replica. Amazon Music Stream millions of songs. The book is divided into four main parts: the first deals with the basics of the charge-coupled devices in general. The second explains the imaging concepts in close relation to the classical television application. Part three goes into detail on new developments in the solid-state imaging world light sensitivity, noise, device architectures , and part four rounds off the discussion with a variety of applications and the imager technology.

The book is a reference work intended for all who deal with one or more aspects of solid- state imaging: the educational, scientific and industrial world. Graduates, undergraduates, engineers and technicians interested in the physics of solid-state imagers will find the answers to their imaging questions. Read more Read less.

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Save Extra with 3 offers. However, as this choice can be quite challenging, methods for feature selection can be essential. The LASSO , attempts to improve regression performance through the creation of sparse models through variable selection. It is mostly used in combination with least-squares linear regression, in which case it results in the following minimization problem :.

In contrast to ridge regression, where the L 2 -norm of the regularization term is used, LASSO aims at translating most coefficients to zero. In order to actually find the model with the minimal number of non-zero components, one would have to use the so called L 0 -norm of the coefficient vector, instead of the L 1 -norm used in LASSO.

The L 0 -norm of a vector is equal to its number of non-zero elements. However, this problem is non-convex and NP-hard and therefore infeasible from a computational perspective. Furthermore, it is proven that the L 1 -norm is a good approximation in many cases. The minimization problem from Eq. Ghiringhelli et al. Necessarily, physical notions like the units of the primary features constrain the number of combinations. LASSO is then used to reduce the number of features to a point where a brute force combination approach to find the lowest error is possible.

This approach is chosen in order to circumvent the problems pure LASSO faces when treating strongly correlated variables and to allow for non-linear models.

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Sure independence screening selects a subspace of features based on their correlation with the target variable and allows for extremely high-dimensional starting spaces. The selected subspace is than further reduced by applying the sparsifying operator e. Predicting the relative stability of octet binary materials as either rock-salt or zincblende was used as a benchmark.

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The first step of bootstrapped-projected gradient descent consists in clustering the features in order to combat the problems other algorithms like LASSO face when encountering strongly correlated features. The features in every cluster are combined in a representative feature for every cluster. In the following, the sparse linear fit problem is approximated with projected gradient descent for different levels of sparsity.

This process is also repeated for various bootstrap samples in order to further reduce the noise. Finally, the intersection of the selected feature sets across the bootstrap samples is chosen as the final solution.

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PCA , extracts the orthogonal directions with the greatest variance from a dataset, which can be used for feature selection and extraction. This is achieved by diagonalizing the covariance matrix. Sorting the eigenvectors by their eigenvalues i. The broad idea behind this scheme is that, in contrast to the original features, the principal components will be uncorrelated. Furthermore, one expects that a small number of principal components will explain most of the variance and therefore provide an accurate representation of the dataset.

Naturally, the direct application of PCA should be considered feature extraction, instead of feature selection, as new descriptors in the form of the principal components are constructed. On the other hand, feature selection based on PCA can follow various strategies. For example, one can select the variables with the highest projection coefficient from, respectively, the first n principal components when selecting n features. A more in-depth discussion of such strategies can be found in ref. The previous algorithms can be considered as linear models or linear models in a kernel space.

An important family of non-linear machine learning algorithms is composed by decision trees. In general terms, decision trees are graphs in tree form, where each node represents a logic condition aiming at dividing the input data into classes see Fig. The optimal splitting conditions are determined by some metric, e. In order to avoid the tendency of simple decision trees to overfit, ensembles such as RFs or extremely randomized trees are used in practice.

Instead of training a single decision tree, multiple decision trees with a slightly randomized training process are built independently from each other.

This randomization can include, for example, using only a random subset of the whole training set to construct the tree, using a random subset of the features, or a random splitting point when considering an optimal split. The final regression or classification result is usually obtained as an average over the ensemble. In this way, additional noise is introduced into the fitting process and overfitting is avoided. In general, decision tree ensemble methods are fast and simple to train as they are less reliant on good hyperparameter settings than most other methods.

Furthermore, they are also feasible for large datasets. A further advantage is their ability to evaluate the relevance of features through a variable importance measure, allowing a selection of the most relevant features and some basic understanding of the model.

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Broadly speaking, these are based on the difference in performance of the decision tree ensemble by including and excluding the feature. This can be measured, e. Extremely randomized trees are usually superior to RFs in higher variance cases as the randomization decreases the variance of the total model and demonstrate at least equal performances in other cases. This proved true for several applications in materials science where both methods were compared. Boosting methods generally combine a number of weak predictors to create a strong model.

In contrast to, e. Commonly used methods, especially in combination with decision tree methods, are gradient boosting , and adaptive boosting. Ranging from feed-forward neural networks over self-organizing maps up to Boltzmann machines and recurrent neural networks, there is a wide variety of neural network structures. However, until now only feed-forward networks have found applications in materials science even if some Boltzmann machines are used in other areas of theoretical physics In brief, a neural network starts with an input layer, continues with a certain number of hidden layers, and ends with an output layer.

The weight matrices are the parameters that have to be fitted during the learning process. Usually, they are trained with gradient descent style methods with respect to some loss function usually L 2 loss with L 1 regularization , through a method known as back-propagation. Inspired by biological neurons, sigmoidal functions were classically used as activation functions. However, as the gradient of the weight-matrix elements is calculated with the chain rule, deeper neural networks with sigmoidal activation functions quickly lead to a vanishing gradient, hampering the training process.

Modern activation functions such as rectified linear units , The real success story of neural networks only started once convolutional neural networks were introduced to image recognition.