Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://accord-framework.net/docs/html/N_Accord_MachineLearning.htm below:

Common interface for Bag of Words objects.

Common interface for classification models. Classification models learn how to produce a class-label (or a set of class labels) y from an input vector x.

Common interface for classification models. Classification models learn how to produce a class-label (or a set of class labels) y from an input vector x.

Common interface for generative binary classifiers. A binary classifier can predict whether or not an instance belongs to a class, while at the same time being able to provide the probability of this sample belonging to the positive class.

Common interface for score-based binary classifiers. A binary classifier can predict whether or not an instance belongs to a class based on a decision score (a real number) that measures the association of the input with the negative and positive class.

Common interface for clusters that contains centroids which are of the same data type as the clustered data types (i.e.

).

Common interface for clusters that contains centroids, where the centroid data type might be different from the data type of the data bring clustered (i.e.

).

Common interface for classification models. Classification models learn how to produce a class-label (or a set of class labels) y from an input vector x.

Common interface for classification models. Classification models learn how to produce a class-label (or a set of class labels) y from an input vector x.

Common interface for cluster collections.

Common interface for cluster collections.

Common interface for collections of clusters (i.e.

,

,

).

Common interface for clustering algorithms.

Common interface for clustering algorithms.

Common interface for data transformation algorithms. Examples of transformations include

,

and other machine learning techniques.

Common interface for unsupervised learning algorithms.

Exploration policy interface.

Common interface for generative models.

Common interface for generative observation sequence taggers. A sequence tagger can predict the class label of each individual observation in a input sequence vector.

Common interface for multi-class models. Classification models learn how to produce a class-label y from an input vector x.

Common interface for multi-class models. Classification models learn how to produce a class-label y from an input vector x.

Common interface for multi-class models. Classification models learn how to produce a class-label y from an input vector x.

Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.

Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.

Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.

Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.

Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.

Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.

Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.

Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.

Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.

Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.

Common interface for multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.

Common interface for multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.

Common interface for multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.

Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once, as well as their probabilities.

Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.

Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.

Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once, as well as their probabilities.

Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.

Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once, as well as their probabilities.

Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.

Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.

Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.

Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.

Common interface for multiple regression models. Multiple regression models learn how to produce a set of real values (a real-valued vector) from an input vector x.

Common interface for multiple regression models. Multiple regression models learn how to produce a set of real values (a real-valued vector) from an input vector x.

Common interface for data transformation algorithms. Examples of transformations include

,

and other machine learning techniques.

Common interface for parallel algorithms.

Common interface for regression models. Regression models learn how to produce a real value (or a set of real values) y from an input vector x.

Common interface for regression models. Regression models learn how to produce a real value (or a set of real values) y from an input vector x.

Common interface for observation sequence taggers.

Common interface for supervised learning algorithms for

.

Common interface for supervised learning algorithms for

.

Common interface for supervised learning algorithms.

Common interface for supervised learning algorithms for

.

Common interface for supervised learning algorithms for

.

Common interface for supervised learning algorithms for

.

Common interface for supervised learning algorithms for

.

Common interface for algorithms that can be canceled in the middle of execution.

Common interface for generative observation sequence taggers. A sequence tagger can predict the class label of each individual observation in a input sequence vector.

Common interface for data transformation algorithms. Examples of transformations include

,

and other machine learning techniques.

Common interface for data transformation algorithms. Examples of transformations include

,

and other machine learning techniques.

Common interface for data transformation algorithms. Examples of transformations include

,

and other machine learning techniques.

Common interface for unsupervised learning algorithms.

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4