A taxonomy of weight learning methods for statistical relational learning

Machine Learning - Statistical relational learning (SRL) frameworks are effective at defining probabilistic models over complex relational data. They often use weighted first-order logical rules...     

Integrating statistical and inductive learning methods for knowledge acquisition

Inductive learning is a method for automated knowledge acquisition. It converts a set of training data into a knowledge structure. In the process of knowledge induction, statistical techniques can play a major role in improving performance. In this paper, we investigate the competition and integration between the traditional statistical and the inductive learning methods. First, the competition between these two approaches is examined. Then, a general framework for integrating these two approaches is presented. This framework suggests three possible integrations: (1) statistical methods as preprocessors for inductive learning, (2) inductive learning methods as preprocessors for statistical classification, and (3) the combination of the two methods to develop new algorithms. Finally, empirical evidence concerning these three possible integrations are discussed. The general conclusion is that algorithms integrating statistical and inductive learning concepts are likely to make the most improvement in performance.     

Long-term time-series pollution forecast using statistical and deep learning methods

Neural Computing and Applications - Tackling air pollution has become of utmost importance since the last few decades. Different statistical as well as deep learning methods have been proposed till...     

Comparative analysis of statistical and machine learning methods for predicting faulty modules

The demand for development of good quality software has seen rapid growth in the last few years. This is leading to increase in the use of the machine learning methods for analyzing and assessing public domain data sets. These methods can be used in developing models for estimating software quality attributes such as fault proneness, maintenance effort, testing effort. Software fault prediction in the early phases of software development can help and guide software practitioners to focus the available testing resources on the weaker areas during the software development. This paper analyses and compares the statistical and six machine learning methods for fault prediction. These methods (Decision Tree, Artificial Neural Network, Cascade Correlation Network, Support Vector Machine, Group Method of Data Handling Method, and Gene Expression Programming) are empirically validated to find the relationship between the static code metrics and the fault proneness of a module. In order to assess and compare the models predicted using the regression and the machine learning methods we used two publicly available data sets AR1 and AR6. We compared the predictive capability of the models using the Area Under the Curve (measured from the Receiver Operating Characteristic (ROC) analysis). The study confirms the predictive capability of the machine learning methods for software fault prediction. The results show that the Area Under the Curve of model predicted using the Decision Tree method is 0.8 and 0.9 (for AR1 and AR6 data sets, respectively) and is a better model than the model predicted using the logistic regression and other machine learning methods.     

The DDoS attacks detection through machine learning and statistical methods in SDN

The Journal of Supercomputing - The distributed denial-of-service (DDoS) attack is a security challenge for the software-defined network (SDN). The different limitations of the existing DDoS...     

A complete characterization of statistical query learning with applications to evolvability

Statistical query (SQ) learning model of Kearns is a natural restriction of the PAC learning model in which a learning algorithm is allowed to obtain estimates of statistical properties of the examples but cannot see the examples themselves (Kearns, 1998 [29]). We describe a new and simple characterization of the query complexity of learning in the SQ learning model. Unlike the previously known bounds on SQ learning (Blum, et al., 1994; Bshouty and Feldman, 2002; Yang, 2005; Balcázar, et al., 2007; Simon, 2007 [9], [11], [42], [3], [37]) our characterization preserves the accuracy and the efficiency of learning. The preservation of accuracy implies that our characterization gives the first characterization of SQ learning in the agnostic learning framework of Haussler (1992) [23] and Kearns, Schapire and Sellie (1994) [31]. The preservation of efficiency is achieved using a new boosting technique and allows us to derive a new approach to the design of evolution algorithms in Valiant?s model of evolvability (Valiant, 2009 [40]). We use this approach to demonstrate the existence of a large class of monotone evolution algorithms based on square loss performance estimation. These results differ significantly from the few known evolution algorithms and give evidence that evolvability in Valiant?s model is a more versatile phenomenon than there had been previous reason to suspect.     

3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) on a series of agonists of thyroid hormone receptor beta (TRbeta), which may lead to safe therapies for non-thyroid disorders while avoiding the cardiac side effects. The reasonable q(2) (cross-validated) values 0.600 and 0.616 and non-cross-validated r(2) values of 0.974 and 0.974 were obtained for CoMFA and CoMSIA models for the training set compounds, respectively. The predictive ability of two models was validated using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)(2)) of 0.688 and 0.674, respectively. The Lamarckian Genetic Algorithm (LGA) of AutoDock 4.0 was employed to explore the binding mode of the compound at the active site of TRbeta. The results not only lead to a better understanding of interactions between these agonists and the thyroid hormone receptor beta but also can provide us some useful information about the influence of structures on the activity which will be very useful for designing some new agonist with desired activity.     

统计学习理论的原理与应用

经验风险与实际风险间的不一致是一个长期困扰机器学习（各种分类或拟合问题）的难题。统计学习理论提供了对这一问题的部分解决方法。本文从理论及现实两方面介绍经验风险与实际风险间的不一致现象，定义了算法的泛化能力，简单介绍了统计学习理论各组成部分的主要结论，并总结了这一理论的应用方向和存在的问题。     

The evaluation of statistical process control methods by simulation

A set of functions that provide for Monte Carlo simulation and graphical analysis of various statistical process control methods and measures is described. the functions are written to the MathCAD microcomputer environment.     

Steels classification by machine learning and Calphad methods

Steels of different classes (austenitic, martensitic, pearlitic, etc.) have different applications and characteristic areas of properties. In the present work two methods are used to predict steel class, based on the composition and heat treatment parameters: the physically-based Calphad method and data-driven machine learning method. They are applied to the same dataset, collected from open sources (mostly steels for high-temperature applications). Classification accuracy of 93.6% is achieved by machine learning model, trained on the concentration of three elements (C, Cr, Ni) and heat treatment parameters (heating temperatures). Calphad method gives 76% accuracy, based on the temperature and cooling rate. The reasons for misclassification by both methods are discussed, and it is shown that the part of them caused by ambiguity/inaccuracy in the data or limitations of the models used. For the rest of cases reasonable classification accuracy is demonstrated. We suggest that the reason of the supremacy of machine learning classifier is the small variation in the data used, which indeed does not change the steel class: the properties of steel should be insensitive to the details of the manufacturing process.     

Three-dimensional molecular field analyses of octopaminergic agonists and antagonists for the locust neuronal octopamine receptor class 3

The quantitative structure-activity relationship (QSAR) of a set of 70 octopaminergic agonists and 20 antagonists against octopamine receptor class 3 (OAR3) in locust nervous tissue was analyzed by molecular field analysis (MFA). MFA of these compounds evaluated effectively the energy between a probe and a molecular model at a series of points defined by a rectangular grid. Contour surfaces for the molecular fields are presented. These results provide useful information in the characterization and differentiation of octopaminergic receptor types and subtypes.     

Use of binding energy in comparative molecular field analysis of isoform selective estrogen receptor ligands

A diverse set of 30 estrogen receptor ligands whose relative binding affinities (RBA) with respect to 17beta-estradiol were available in both isoforms of the nuclear estrogen receptor (ERalpha, ERbeta) were studied with a combination of comparative molecular field analysis (CoMFA) and binding energy calculations. The ligands were docked inside the ligand-binding domain (LBD) of both ERalpha and ERbeta utilizing the docking program Gold. The binding energy (DeltaE) and corresponding non-bonded interactions (NB) of the subsequent protein-ligand complexes were calculated in both the gas-phase and implicit aqueous solution using the generalized born surface area (GB/SA) model. A partial least-squares analysis of the calculated energies indicated that the NB(g) were sufficiently predictive in ERalpha, but performed poorly in ERbeta. Further analysis of the calculated energies by dissecting the ligands into two distinct classes, estrogen-like and heterocyclic, yielded more predictive models. In particular the DeltaE calculated in solution proved particularly predictive for the estrogen-like ligands in ERbeta. Finally the estrogen subtype selective nature RBA (ERalpha/ERbeta) of a test-set consisting of six of the original ligands was predicted. The combined CoMFA and non-bonded interaction energy model ranked correctly the ligands in order of increasing RBA (ERalpha/ERbeta), illustrating the utility of this method as a prescreening tool in the development of novel estrogen receptor subtype selective ligands.