[PDF] Top 20 Active learning methods for classification and regression problems

... . In [8], the authors propose a preliminary filtering procedure. A sample is suspect when in its neighbourhood defined by a geometrical graph the portion of examples of the same class is not significantly greater than in ... See full document

... these methods effectively and interpreting properly the results means to know the underlying fundamental principles, which they are based ...networks methods are based on paying special attention to the ... See full document

... ensemble learning for regression in the context of neural networks, however there has been less research carried out in terms of using homogeneous ensemble techniques to improve the performance of simple ... See full document

... a regression model depends on the matchup of predictions against actual ...In regression problems, the dependent variable is ...In classification problems, the dependent variable is ... See full document

... and Regression Tree) is one of the popular methods of building decision trees in the machine learning community; CART builds a binary decision tree by splitting the record at each separate node, ... See full document

... the classification algorithm ...extraction methods like Principal Component Analysis [13], Correlation Based Feature Selection ...Machine learning algorithms are applied on this dataset including the ... See full document

... of active learning. The basic idea of active learning is to learn an accurate classifier using less training ...An active learner uses a small set of labeled data to iteratively select ... See full document

... existing methods treat these problems as linear regression, learning to relate word frequencies and other simple features to a known response variable ... See full document

... We used the default complexity parameters for logistic regression and support vector machines and used Laplace smoothing for multinomial na¨ıve Bayes. In our rationale framework, most features were non-rationales, ... See full document

... for active learning in binary classification, where one estimates the class-conditional regression function (Castro and Nowak, 2008; Minsker, 2012; Hanneke and Yang, 2012; Car- pentier et ... See full document

... The main insight of this paper is (a) to consider the detailed face structure of the base polytope B(F) and (b) to notice that for the outer approximation of B(F ) based on the tangent cone to a certain face, the ... See full document

... of active learning to perfect selective classification that preserves “fast ...dynamic active learning problems as static ...selective classification from El-Yaniv and ... See full document

... simultaneously, so the neuron receives more than one input signal, but it produces only one output signal. Every input signal is associated with a connection weight. The weight determines the relative importance the ... See full document

... using classification we can group ...using classification we can decide to which class the new item belong ...in classification to achieve maximum accuracy. Each classification technique has ... See full document

... For the learning task Alofs and Latour used a mix of features. They used the complete sentence as a feature. They used a list of all unigrams, bigrams and trigrams of tokens in a sentence as feature. Also the ... See full document

... Logistics Regression using standard datasets like Automobile, Contact-lenses, Thyroid, Housing ...Logistic Regression with Naive Bayes by comparing Naive Bayes and Logistic ...various classification ... See full document

... nonlinear regression problems without the assumption of being independent and identically ...nonlinear regression and give a numerical ...in regression and machine learning ... See full document

... kernel learning (MKL) aims at simultaneously learning a kernel and the associated predic- tor in supervised learning ...kernel learning algorithm, based on semi-infinite linear programming, ... See full document

... unsupervised learning techniques and exploitation of the similarities between data [15, 16, ...competitive learning, a process where all the output neurons compete with one ... See full document

... machine learning approaches that have been adopted for solving issues in the health care ...machine learning algorithms that are used to tackle issues in the field of ...machine learning. ... See full document