Classifier learning algorithm

These features are then passed on to machine learning algorithms to classify the signals. In particular, multifractal properties are a clear indicator of arrhythmia in ECG signals. The onset of deformity in the ECG signal has a signature pattern that can be used to predict the onset of a heart attack. Scinova proposes to exploit this feature of Rx as an early warning system for intensive care units. 

The perceptron is a learning algorithm and can be considered as a simple model of a biological neuron. It is worth examining here not only as a classifier in its own right, but also as providing the basic features of modem artificial neural networks. 

Expert feedback mainly consists of correct mappings between the schemas to be matched. These mappings can be seen as a bootstrap for the schema matcher, i.e., knowledge is taken as input by machine learning algorithms to classify schema instances. It may be a compulsory parameter such as in LSD/Glue [Doan et al. 2001, 2003] and APFEL [Ehrig et al. 2005],... 

The two independent diagnostic features of structural ncRNAs, z-score and SCI, are finally used to classify an alignment as structural RNA or other. For this purpose, RNAz uses a support vector machine (SVM) learning algorithm, which is trained on a large test set of well known ncRNAs. 

Tree classifiers are unstable due to unexpected distribution of the real data (Leistner et al. 2009). An ensemble is a supervised learning algorithm, since it is trained first and then used to make predictions. The trained ensemble represents a single hypothesis. Empirically, ensembles tend to yield better results when there is significant diversity among the models (Kuncheva Whitaker 2003). Therefore, ensemble methods are normally designed to promote diversity among the models in their combination. 

It should be pointed out that this approach can t strictly be used for TTS purposes as acoustic features (e.g. time in seconds) measured from the corpus waveforms were used in addition to features that would be available at run time. Following this initial work, a number of studies have used decision trees [264] [418], and a wide variety of other machine learning algorithms have been applied to the problem including memory based learning [77] [402], Bayesian classifiers [516], support vector machines [87] and neural networks [157]. Similar results are reported in most cases, and it seems that the most important factors in the success of a system are the features used and the quality and quantity of data rather than the particular machine learning algorithm used. 

The decision tree classifier is chosen for its favorable tradeoff between performance and implementation simplicity. Classification using DT is a supervised learning technique, the input of the learning algorithm is a set of known data and the output is a tree model similar to the ones shown in Figure 5. Once the tree is defined, the classification of new inputs starts at the root decision node of the tree and terminates at one of the leaf nodes that represent a specific class, passing by intermediate decision nodes. 

In most of the cases of interest, the information that is sought is related to defining the failure boundaries of a system with respect to perturbations in the input space. For this reason, in the development of RAVEN, it has been given priority to the introduction of a class of supervised learning algorithms, which are usually referred to as classifiers. A classifier is a reduced order model that is capable of representing the system behavior through a binary response (failure/success). 

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