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Cries of infants can be seen as an indicator for several developmental diseases. Different types of classification algorithms have been used in the past to classify infant cries of healthy infants and those with developmental diseases. To determine the ability of classification models to discriminate between healthy infant cries and various cries of infants suffering from several diseases, a literature search for infant cry classification models was performed; 9 classification models were identified that have been used for infant cry classification in the past. These classification models, as well as 3 new approaches were applied to a reference dataset containing cries of healthy infants and cries of infants suffering from laryngomalacia, cleft lip and palate, hearing impairment, asphyxia and brain damage. Classification models were evaluated according to a rating schema, considering the aspects accuracy, degree of overfitting and conformability. Results indicate that many models have issues with accuracy and conformability. However, some of the models, like C5.0 decision trees and J48 classification trees provide promising results in infant cry classification for diagnostic purpose.
supervised-learning model review, Fuhr et al. (2015) showed the classification models which have been used in the past to classify infant cries and evaluated the applicability of these classification models. Hence, computer-based algorithms were suited to classify infant cries. But considering the performance of machines in the field of language detection, the human abilities were much better than the machine-based abilities on language and speech identification ( Norvig, 2012 ; Luxton, 2016 ).
Studies investigating the association of specific cry characteristics to
Simulation 41(1): 3-12(10).
Kasiński A. and Kraft M. (2006). The design of a compact LIF-neuron circuit in FPGA to enable implementation of largescale spiking neuron networks with learning capabilities, Proceesings of the International Conference on Artificial Intelligence and Soft Computing, ICAISC'2006 , Warsaw, Poland, pp. 57-64.
Kasiński A. and Ponulak F. (2005). Experimental demonstration of learning properties of a new supervisedlearning method for the spiking neural networks, Lecture Notes in Computer Science
Gang Mu, Teodor Godina, Antonio Maffia and Yong Chao Sun
In this paper, we make use of a Bayesian (supervised learning) approach in pricing American options via Monte Carlo simulations. We first present Gaussian process regression (Kriging) approach for American options pricing and compare its performance in estimating the continuation value with the Longstaff and Schwartz algorithm. Secondly, we explore the control variates technique in combination with Kriging to further improve the estimation of the continuation value. This method allows to reduce dramatically the standard errors and to improve the stability of the Kriging approach. For illustrative purposes, we use American put options on a stock whose dynamics is given by Heston model, and use European options on the same stock as control variates.
Feature extraction is an interactive and iterative analysis process of a large dataset of raw data in order to extract meaningful knowledge. In this article, we present a strong descriptor based on the Discrete Cosine Transform (DCT), we show that the new DCT-based Neighboring Support Vector Classifier (DCT-NSVC) provides a better results compared to other algorithms for supervised classification. Experiments on our real dataset named BOSS, show that the accuracy of classification has reached 99%. The application of DCT-NSVC on MIT-CBCL dataset confirms the performance of the proposed approach.
Muhammad Rizwan Rashid Rana, Asif Nawaz and Javed Iqbal
Sentiment classification is the process of exploring sentiments, emotions, ideas and thoughts in the sentences which are expressed by the people. Sentiment classification allows us to judge the sentiments and feelings of the peoples by analyzing their reviews, social media comments etc. about all the aspects. Machine learning techniques and Lexicon based techniques are being mostly used in sentiment classification to predict sentiments from customers reviews and comments. Machine learning techniques includes several learning algorithms to judge the sentiments i.e Navie bayes, support vector machines etc whereas Lexicon Based techniques includes SentiWordnet, Wordnet etc. The main target of this survey is to give nearly full image of sentiment classification techniques. Survey paper provides the comprehensive overview of recent and past research on sentiment classification and provides excellent research queries and approaches for future aspects
A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. The experimental results revealed that (i) the Bode-Chaudhuri-Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.
This paper presents a supervised approach to the recognition of Cross-document Structure Theory (CST) relations in Polish texts. Its core is a graph-based representation constructed for sentences. Graphs are built on the basis of lexicalised syntactic-semantic relations extracted from text. Similarity between sentences is calculated as similarity between their graphs, and the values are used as features to train the classifiers. Several different configurations of graphs, as well as graph similarity methods were analysed for this task. The approach was evaluated on a large open corpus annotated manually with 17 types of selected CST relations. The configuration of experiments was similar to those known from SEMEVAL and we obtained very promising results.