Estimating the market share attraction model using support vector regressions.

We propose to estimate the parameters of the Market Share Attraction Model (Cooper & Nakanishi, 1988; Fok & Franses, 2004) in a novel way by using a non-parametric technique for function estimation called Support Vector Regressions (SVR)(Vapnik, 1995; Smola, 1996). Traditionally, the parameters of the Market Share Attraction Model are estimated via a Maximum Likelihood (ML) procedure, assuming that the data are drawn from a conditional Gaussian distribution. However, if the distribution is unknown, ML estimation may seriously fail (Vapnik, 1982). One way to tackle this problem is to introduce a linear loss function over the errors and a penalty on the magnitude of model coefficients. This leads to qualities such as robustness to outliers and avoidance of the problem of over¯tting. This kind of estimation forms the basis of the SVR technique, which, as we will argue, makes it a good candidate for solving the Market Share Attraction Model. We test the SVR approach to predict (the evolution of) the market shares of 36 car brands simultaneously and report stronger results than when using a ML estimation procedure.

Using Machine Learning for Analysis a Database Outdoor Monitoring of Photovoltaic System

: In this paper we propose a new method for analyzing the performance of photovoltaic system using classification, the monitoring of photovoltaic module (150 W) was controlled and analyzed, the system was deployed in Algiers over a long period (80 days), one of the most important difficulties faced by researchers is collecting and analyzing the results of monitoring for a long period, so in this paper we proposed a method for analyzing results by classification using SVM Classifier. More specifically, we regrouping a data variable to multiclass for according and analyzing using SVM. We have presented thoroughly all the calculation steps. Based on the application of artificial intelligence (classification), recorded data, the power output for a given solar panels technology, types and small or large stations under any seasons can be analyzed and treated easily. The several measurements in our laboratory was investigated based on data acquisition (Keysight 34972A).The system collects the measurements from the various sensors. The measurement system was taken the data between 05h00 to 21h00 with irradiation of 50 W/m2 which is starting point, however in 0 to 50 W/m2 the system cannot detect any photovoltaic effect. Results predict that the performance ratio (PR) from a Poly-crystalline panel was around 85.28 % for a different season’s exposure and 727 point analyzes at irradiation of 850-950 W/m2 in same time 14h00-15h00 . The temperature of solar panel are also calculated and compared in different irradiation and time

Using Machine Learning for Analysis a Database Outdoor Monitoring of Photovoltaic System

: In this paper we propose a new method for analyzing the performance of photovoltaic system using classification, the monitoring of photovoltaic module (150 W) was controlled and analyzed, the system was deployed in Algiers over a long period (80 days), one of the most important difficulties faced by researchers is collecting and analyzing the results of monitoring for a long period, so in this paper we proposed a method for analyzing results by classification using SVM Classifier. More specifically, we regrouping a data variable to multiclass for according and analyzing using SVM. We have presented thoroughly all the calculation steps. Based on the application of artificial intelligence (classification), recorded data, the power output for a given solar panels technology, types and small or large stations under any seasons can be analyzed and treated easily. The several measurements in our laboratory was investigated based on data acquisition (Keysight 34972A).The system collects the measurements from the various sensors. The measurement system was taken the data between 05h00 to 21h00 with irradiation of 50 W/m2 which is starting point, however in 0 to 50 W/m2 the system cannot detect any photovoltaic effect. Results predict that the performance ratio (PR) from a Poly-crystalline panel was around 85.28 % for a different season’s exposure and 727 point analyzes at irradiation of 850-950 W/m2 in same time 14h00-15h00 . The temperature of solar panel are also calculated and compared in different irradiation and time

Hierarchical Multi-Bottleneck Classification Method And Its Application to DNA Microarray Expression Data

The recent development of DNA microarray technology is creating a wealth of gene expression data. Typically these datasets have high dimensionality and a lot of varieties. Analysis of DNA microarray expression data is a fast growing research area that interfaces various disciplines such as biology, biochemistry, computer science and statistics. It is concluded that clustering and classification techniques can be successfully employed to group genes based on the similarity of their expression patterns. In this paper, a hierarchical multi-bottleneck classification method is proposed, and it is applied to classify a publicly available gene microarray expression data of budding yeast Saccharomyces cerevisiae.Singapore-MIT Alliance (SMA

Nonparallel support vector machines for pattern classification

We propose a novel nonparallel classifier, called nonparallel support vector machine (NPSVM), for binary classification. Our NPSVM that is fully different from the existing nonparallel classifiers, such as the generalized eigenvalue proximal support vector machine (GEPSVM) and the twin support vector machine (TWSVM), has several incomparable advantages: 1) two primal problems are constructed implementing the structural risk minimization principle; 2) the dual problems of these two primal problems have the same advantages as that of the standard SVMs, so that the kernel trick can be applied directly, while existing TWSVMs have to construct another two primal problems for nonlinear cases based on the approximate kernel-generated surfaces, furthermore, their nonlinear problems cannot degenerate to the linear case even the linear kernel is used; 3) the dual problems have the same elegant formulation with that of standard SVMs and can certainly be solved efficiently by sequential minimization optimization algorithm, while existing GEPSVM or TWSVMs are not suitable for large scale problems; 4) it has the inherent sparseness as standard SVMs; 5) existing TWSVMs are only the special cases of the NPSVM when the parameters of which are appropriately chosen. Experimental results on lots of datasets show the effectiveness of our method in both sparseness and classification accuracy, and therefore, confirm the above conclusion further. In some sense, our NPSVM is a new starting point of nonparallel classifiers

Spot Detection and Image Segmentation in DNA Microarray Data

Following the invention of microarrays in 1994, the development and applications of this technology have grown exponentially. The numerous applications of microarray technology include clinical diagnosis and treatment, drug design and discovery, tumour detection, and environmental health research. One of the key issues in the experimental approaches utilising microarrays is to extract quantitative information from the spots, which represent genes in a given experiment. For this process, the initial stages are important and they influence future steps in the analysis. Identifying the spots and separating the background from the foreground is a fundamental problem in DNA microarray data analysis. In this review, we present an overview of state-of-the-art methods for microarray image segmentation. We discuss the foundations of the circle-shaped approach, adaptive shape segmentation, histogram-based methods and the recently introduced clustering-based techniques. We analytically show that clustering-based techniques are equivalent to the one-dimensional, standard k-means clustering algorithm that utilises the Euclidean distance

Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs)

We present a new support vector machine (SVM)-based approach to predict the substrate specificity of subtypes of a given protein sequence family. We demonstrate the usefulness of this method on the example of aryl acid-activating and amino acid-activating adenylation domains (A domains) of nonribosomal peptide synthetases (NRPS). The residues of gramicidin synthetase A that are 8 Å around the substrate amino acid and corresponding positions of other adenylation domain sequences with 397 known and unknown specificities were extracted and used to encode this physico-chemical fingerprint into normalized real-valued feature vectors based on the physico-chemical properties of the amino acids. The SVM software package SVM(light) was used for training and classification, with transductive SVMs to take advantage of the information inherent in unlabeled data. Specificities for very similar substrates that frequently show cross-specificities were pooled to the so-called composite specificities and predictive models were built for them. The reliability of the models was confirmed in cross-validations and in comparison with a currently used sequence-comparison-based method. When comparing the predictions for 1230 NRPS A domains that are currently detectable in UniProt, the new method was able to give a specificity prediction in an additional 18% of the cases compared with the old method. For 70% of the sequences both methods agreed, for <6% they did not, mainly on low-confidence predictions by the existing method. None of the predictive methods could infer any specificity for 2.4% of the sequences, suggesting completely new types of specificity