Was Bedeutet Features "feature" Deutsch Übersetzung
Allgemein wird ein. der Mustererkennung ist ein Merkmal eine individuell messbare Eigenschaft oder Charakteristik eines beobachteten Phänomens. Siehe auch Slow Feature. Das Feature (engl. feature „Merkmal“, „Charakteristik“) ist eine journalistische Darstellungsform. Features enthalten sowohl Merkmale einer Reportage als auch. Feature beim Online Wöpaopractinet.nl: ✓ Bedeutung, ✓ Definition, ✓ Übersetzung, ✓ Herkunft, ✓ Rechtschreibung, ✓ Silbentrennung. Brevity is a typical feature of technical texts. — Kürze ist ein typisches Merkmal für technische Texte. Eigenschaft f.
Es ist das Partizip Präsens vom Verb "to feature". Wie bei den meisten englischen Verben wurde es durch den Wortstamm "feat" plus Endung. Definition, Rechtschreibung, Synonyme und Grammatik von 'Feature' auf Duden online nachschlagen. Wörterbuch der deutschen Sprache. der Mustererkennung ist ein Merkmal eine individuell messbare Eigenschaft oder Charakteristik eines beobachteten Phänomens. Siehe auch Slow Feature.
The Romanesque school of the Rhine had derived the feature from the early chapels of Rome. Tall and lithe of form, straight of feature was the Israelite king.
The organ of speech still more animates this part, and gives it more life than any other feature in the face.
It is something that pleases him very much, Violet decides, and a delicious interest brightens every feature. In some instances this float idea is made so pronounced a feature of the machine that it becomes a flying boat.
Also called feature film. Older Use. In machine learning, this is typically done by cross-validation. In statistics, some criteria are optimized.
This leads to the inherent problem of nesting. More robust methods have been explored, such as branch and bound and piecewise linear network. Subset selection evaluates a subset of features as a group for suitability.
Subset selection algorithms can be broken up into wrappers, filters, and embedded methods. Wrappers use a search algorithm to search through the space of possible features and evaluate each subset by running a model on the subset.
Wrappers can be computationally expensive and have a risk of over fitting to the model. Filters are similar to wrappers in the search approach, but instead of evaluating against a model, a simpler filter is evaluated.
Embedded techniques are embedded in, and specific to, a model. Many popular search approaches use greedy hill climbing , which iteratively evaluates a candidate subset of features, then modifies the subset and evaluates if the new subset is an improvement over the old.
Evaluation of the subsets requires a scoring metric that grades a subset of features. Exhaustive search is generally impractical, so at some implementor or operator defined stopping point, the subset of features with the highest score discovered up to that point is selected as the satisfactory feature subset.
The stopping criterion varies by algorithm; possible criteria include: a subset score exceeds a threshold, a program's maximum allowed run time has been surpassed, etc.
Alternative search-based techniques are based on targeted projection pursuit which finds low-dimensional projections of the data that score highly: the features that have the largest projections in the lower-dimensional space are then selected.
Two popular filter metrics for classification problems are correlation and mutual information , although neither are true metrics or 'distance measures' in the mathematical sense, since they fail to obey the triangle inequality and thus do not compute any actual 'distance' — they should rather be regarded as 'scores'.
These scores are computed between a candidate feature or set of features and the desired output category.
There are, however, true metrics that are a simple function of the mutual information;  see here. The choice of optimality criteria is difficult as there are multiple objectives in a feature selection task.
Many common criteria incorporate a measure of accuracy, penalised by the number of features selected. Examples include Akaike information criterion AIC and Mallows's C p , which have a penalty of 2 for each added feature.
AIC is based on information theory , and is effectively derived via the maximum entropy principle. A maximum entropy rate criterion may also be used to select the most relevant subset of features.
Filter feature selection is a specific case of a more general paradigm called Structure Learning. Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph.
The most common structure learning algorithms assume the data is generated by a Bayesian Network , and so the structure is a directed graphical model.
The optimal solution to the filter feature selection problem is the Markov blanket of the target node, and in a Bayesian Network, there is a unique Markov Blanket for each node.
There are different Feature Selection mechanisms around that utilize mutual information for scoring the different features.
They usually use all the same algorithm:. The simplest approach uses the mutual information as the "derived" score. Peng et al.
The aim is to penalise a feature's relevancy by its redundancy in the presence of the other selected features. The relevance of a feature set S for the class c is defined by the average value of all mutual information values between the individual feature f i and the class c as follows:.
The redundancy of all features in the set S is the average value of all mutual information values between the feature f i and the feature f j :.
Suppose that there are n full-set features. The above may then be written as an optimization problem:. The mRMR algorithm is an approximation of the theoretically optimal maximum-dependency feature selection algorithm that maximizes the mutual information between the joint distribution of the selected features and the classification variable.
As mRMR approximates the combinatorial estimation problem with a series of much smaller problems, each of which only involves two variables, it thus uses pairwise joint probabilities which are more robust.
In certain situations the algorithm may underestimate the usefulness of features as it has no way to measure interactions between features which can increase relevancy.
This can lead to poor performance  when the features are individually useless, but are useful when combined a pathological case is found when the class is a parity function of the features.
Overall the algorithm is more efficient in terms of the amount of data required than the theoretically optimal max-dependency selection, yet produces a feature set with little pairwise redundancy.
While mRMR could be optimized using floating search to reduce some features, it might also be reformulated as a global quadratic programming optimization problem as follows: .
QPFS is solved via quadratic programming. Another score derived for the mutual information is based on the conditional relevancy: .
In a study of different scores Brown et al. The score tries to find the feature, that adds the most new information to the already selected features, in order to avoid redundancy.
The score is formulated as follows:. For high-dimensional and small sample data e. HSIC always takes a non-negative value, and is zero if and only if two random variables are statistically independent when a universal reproducing kernel such as the Gaussian kernel is used.
The optimization problem is a Lasso problem, and thus it can be efficiently solved with a state-of-the-art Lasso solver such as the dual augmented Lagrangian method.
The correlation feature selection CFS measure evaluates subsets of features on the basis of the following hypothesis: "Good feature subsets contain features highly correlated with the classification, yet uncorrelated to each other".
The CFS criterion is defined as follows:. Hall's dissertation uses neither of these, but uses three different measures of relatedness, minimum description length MDL , symmetrical uncertainty , and relief.
Let x i be the set membership indicator function for feature f i ; then the above can be rewritten as an optimization problem:.
The combinatorial problems above are, in fact, mixed 0—1 linear programming problems that can be solved by using branch-and-bound algorithms.
The features from a decision tree or a tree ensemble are shown to be redundant. A recent method called regularized tree  can be used for feature subset selection.
Regularized trees penalize using a variable similar to the variables selected at previous tree nodes for splitting the current node.
Regularized trees only need build one tree model or one tree ensemble model and thus are computationally efficient. Regularized trees naturally handle numerical and categorical features, interactions and nonlinearities.
They are invariant to attribute scales units and insensitive to outliers , and thus, require little data preprocessing such as normalization.
Regularized random forest RRF  is one type of regularized trees. A metaheuristic is a general description of an algorithm dedicated to solve difficult typically NP-hard problem optimization problems for which there is no classical solving methods.
Generally, a metaheuristic is a stochastic algorithm tending to reach a global optimum. There are many metaheuristics, from a simple local search to a complex global search algorithm.
The feature selection methods are typically presented in three classes based on how they combine the selection algorithm and the model building.
Filter type methods select variables regardless of the model. They are based only on general features like the correlation with the variable to predict.
Filter methods suppress the least interesting variables. Words related to features physiognomy , visage , mien , appearance , countenance , face , mug , puss , lineaments.
Example sentences from the Web for features He reminisces about the features of Texas life that make Texas its own, distinctive community.
Will Texas Stay Texan? The Gold-Seekers Gustave Aimard. Denis Dent Ernest W. Inventions in the Century William Henry Doolittle. The Bath Keepers, v.
Also called: feature story a prominent story in a newspaper, etc a feature on prison reform.
See More First Known Use of feature Noun 14th century, in the meaning defined at sense 1a Verb circa , in the meaning defined at transitive sense 1 History and Etymology for feature Noun and Verb Middle English feture , from Anglo-French, from Latin factura act of making, from factus , past participle of facere to make — more at do Keep scrolling for more Learn More about feature Share feature Post the Definition of feature to Facebook Share the Definition of feature on Twitter Time Traveler for feature.
See more words from the same century Dictionary Entries near feature feathery featly featness feature featured feature in feature-length.
Accessed 3 Aug. Keep scrolling for more More Definitions for feature feature. Entry 1 of 2 : an interesting or important part, quality, ability, etc.
Entry 1 of 2 1 : a part as the nose or the mouth of the face 2 : something especially noticeable a safety feature 3 : movie sense 1 4 : a special story in a newspaper or magazine feature.
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Login or Register. Save Word. A feature is also the main movie shown in a movie program. The new smart phone has all the features of existing models , plus added extras.
See also call feature. The latest software features built-in access to online messaging services. Examples of feature.
Many have been focusing its product features -- what it has or doesn't have. From Business Insider. But if you will be in and out in an hour or two these features may have less of an impact.
From ABC News. These examples are from the Cambridge English Corpus and from sources on the web. Any opinions in the examples do not represent the opinion of the Cambridge Dictionary editors or of Cambridge University Press or its licensors.
When people get older, settle down with a spouse and start raising a family, they still consider the home and its features as investments. You already lost myself and many others users over the bad blood regarding this feature and how it was handled.
From CNET. Tap into your smartphone's location features to learn more about the world around you. From CNN. After these tips, please see below for a recipe featuring ginger.
From Huffington Post. Faces are the physical feature people most often focus on. Here's a rundown of the new features, and how you can opt out if you choose.
From Fast Company. Remove it and turnaround time for new features goes down. From TechCrunch. They looked great on that incredible screen -- and the screen really is among the most noteworthy features.
From Gizmodo. He has covered a range of stories from cops to courts, foreclosures to features. From CBS Local. This year at least featured no such shocking oversights.
From The Atlantic. Translations of feature in Chinese Traditional. Need a translator? Translator tool. The Interview, their fifth feature together, is undoubtedly their most controversial.
His first feature film, Jellyfish Eyes, debuted last year and was set in a town near a threatening nuclear power plant. The Romanesque school of the Rhine had derived the feature from the early chapels of Rome.
Tall and lithe of form, straight of feature was the Israelite king. The organ of speech still more animates this part, and gives it more life than any other feature in the face.
It is something that pleases him very much, Violet decides, and a delicious interest brightens every feature.
In some instances this float idea is made so pronounced a feature of the machine that it becomes a flying boat.Beste Spielothek in Stetzsch finden Feature-Schreiber ist deshalb mehr als nur Reporter : Er schildert zwar auch, aber nur zur Illustration dessen, was er darstellen oder erklären will. Postleitzahl Canada kommt ein Wort in den Duden? Sie sind öfter hier? Quelle: Europarl. Impressum Datenschutzerklärung Kontakt. Herkunft und Funktion des Ausrufezeichens. Worttrennung Fea ture. Sendung in Form eines aus Reportagen, Kommentaren und Dialogen zusammengesetzten [Dokumentar]berichtes. Features können jedoch nicht nur in Computer-Programmen auftreten. A learning algorithm takes advantage of its own variable Ingdiba BГ¶rse process and performs feature selection and classification simultaneously, such as the FRMT algorithm. Lecture Notes in Computer Science. The new smart phone has all the features of existing modelsplus added extras. Bibcode : PLoSO. Applications of Evolutionary Computing. Also called: feature story a prominent story in a newspaper, etc a feature on prison reform. The above may then Beste Spielothek in Isenthal finden written as an optimization problem:. The simplest algorithm is to test each possible subset of features finding the one which minimizes Spiele Kostenl error rate.