As reviewed in this article, the multi-dimensional structure of the data can be taken into account to improve further the estimation of the model **order**. As an example of such improvement, we show our pro- posed R-dimensional Exponential Fitting Test (R-D EFT) for multi-dimensional applications, where the noise is additive white Gaussian. The R-D EFT success- fully outperforms the M-EFT confirming that even the technique with the best performance can be improved by taking into account the multi-dimensional structure of the data [1,3,4]. In addition, we also extend our modified versions of AIC and MDL to their respective multi-dimensional versions R-D AIC and R-D MDL. For scenarios with colored noise, we present our proposed multi-dimensional model **order** **selection** technique called closed-form PARAFAC-based model **order** **selection** (CFP-MOS) scheme [3,5].

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There exist other estimators of α (Resnick, 2007), but here we consider Hill and focus on **order** **selection**. We introduce an **order** selector that is optimal in the sense that, for a given estimation problem, it minimizes a root mean squared error (RMSE) measure for b α in an internal (i.e. within the algo- rithm) simulation loop. In our approach, the search for opti- mal k is performed in a brute force manner and adaptively for a data set. Hence, the approach is computationally intensive. Processes in complex geophysical systems may exhibit not only heavy tail behaviour, but also persistence in the time domain. Let t (i) denote a time value and { t (i), x(i) } n

the presence of multiplicative correlated noise with unknown PSD and AWGN [20]. The problem of layover solution can be divided into two subproblems: (i) estimating the num- ber of sources, which is the so-called detection problem or model **order** **selection** problem; (ii) retrieving the parame- ters of each single component, which is the estimation prob- lem. The final appeal for the user of an MB layover solu- tion processing chain strongly depends on the automatic estimation of N s , and accuracy of the overall layover solu- tion depends on the successful determination of the num- ber of signal components. In particular, most of the reported good properties of model-based signal-subspace methods are valid only if the assumed model **order** is the correct one. Also when nonmodel-based (possibly adaptive) spec- tral estimation methods are employed, model **order** has to be selected in the height/reflectivity map reconstruction stage of the layover area from the continuous elevation profiling [7, 10].

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High-resolution methods for estimating signal processing parameters such as bearing angles in array processing or frequencies in spectral analysis may be hampered by the model **order** if poorly selected. As classical model **order** **selection** methods fail when the number of snapshots available is small, this paper proposes a method for noncoherent sources, which continues to work under such conditions, while maintaining low computational complexity. For white Gaussian noise and short data we show that the profile of the ordered noise eigenvalues is seen to approximately fit an exponential law. This fact is used to provide a recursive algorithm which detects a mismatch between the observed eigenvalue profile and the theoretical noise-only eigenvalue profile, as such a mismatch indicates the presence of a source. Moreover this proposed method allows the probability of false alarm to be controlled and predefined, which is a crucial point for systems such as RADARs. Results of simulations are provided in **order** to show the capabilities of the algorithm.

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There are many aspects that we can discuss in system identification such as type of model, the techniques and many more. System identification deals with the problem of building mathematical models of dynamic system based on observed data from the system. The subject is thus part of basic scientific methodology and since dynamical system is abundant in our environment, the techniques of system identification have a wide application area (Ljung., 1987). Besides, the most important in system identification is to achieve model from system data and from the **selection** of error **order** will cause an accurate model. This experiment will discuss about vary the value of error **order** that happen to the accurate model. Since the presence of measurement error **order** only occur in ARMAX model, the discussion about why wrong **selection** of error **order** will cause the developed ARMAX model unable to represent system behaviour.

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these ibex are gregarious and have no natural predators (at least adults), thus these seem to be less important fac- tors [19, 25, 26]. Further, we acknowledge that we dealt mainly with third-**order** habitat **selection**, i.e. **selection** within home range, and second-**order** **selection** related to resource distribution; however, habitat structure and land- scape configuration at both broader and shorter spatial scales might influence space use and home range size by constraining movement at finer temporal scales and coarser spatial scales [8]. We highlight the importance of understanding how space use varies across time. Even finer temporal and spatial scales should provide further insight into habitat **selection** [47] and home range size [48]. For example, how does the importance of habitat se- lection and **selection**-free movement in explaining space use vary with temporal and spatial scale? Do these scaling relationships vary among individuals, species, and related traits? Such knowledge could provide an impartial tool to make comparisons across species and ecosystems that would contribute to delineate general mechanisms of home range behaviour. Still, we note the high proportion of variation explained by our home range size models, which suggest that a significant proportion of space use patterns might be explained by habitat **selection** and movement processes happening at the 4-h temporal scale and home-range spatial scale.

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The judging commission for the competitive examination consisted of three members. This commission cor- rected the exams, considered that 11 of the 24 candidates who took the exams were qualified, and classified them in **order** of merit according to the opinion of March 12, 1881. The Board of Directors of the Inspector General of Elementary and Secondary Education, the body responsible for conducting the exams, designated a Commission responsible for reviewing all work from the competitive examination. Because of the number of candidates and, consequently, the number of written exams, this work could not be conducted during a session of the Council. The Commission appointed by the Council considered that the Examining Commission “had ex- cessive benevolence” and did not “find fair the disqualification” of two of the candidates. Thus, it decided to in- clude these two on the list of qualified members and reformulated the rankings. As observed in the correspon- dence sent by the Inspector of Public Education to the Minister and Secretary of Empire Affairs, this divergence caused conflicts within the Council. One of its members was of the opinion that the Board of Directors did not have the competence to change the decisions of the Examining Commission, which claim was refuted by the President, who, considering himself to be supported by the Regulation, disseminated the list of candidates who were deemed qualified with indications of the schools where they should be hired. In the same correspondence, the Inspector mentions the requirements for five candidates seeking appointment to certain schools. Attached to the list that was sent, there is a list that mentions that some candidates were recommended by the President of the Board and by other doctors or Barons (all those recommended had been considered qualified by the two commissions).

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The main purpose of this paper is to deal with classification algorithm with feature **selection** is used to improve the prediction accuracy in the medical data. This paper applies best first search and greedy search as a searching methods and feature evaluator used as CFS. Naive Bayes classification algorithm is used for hepatitis patients’ dataset. It analyzes the data set taken from the UC Irvine machine learning repository. The result of the classification model is time and improved classification accuracy. Finally, it concludes that the proposed methodology performance is better than other classification algorithms.

implementation of an exploratory procedure that serves to extend the range of possible outcomes of an experiment, thereby enabling it to pursue its objectives. Furthermore, I argue that the present account subsumes the notion of exploratory experimentation, which is often attributed in the relevant literature to the works of Friedrich Steinle and Richard Burian, as a particular type of experimental exploration carried out in the special cases where no well-formed theoretical framework of the phenomena under investigation (yet) exists. I illustrate the present account in the context of the ATLAS experiment at CERN’s Large Hadron Collider, where the long-sought Higgs boson has been discovered in 2012. I argue that the data **selection** procedure carried out in the ATLAS experiment illustrates an exploratory procedure in the sense suggested by the present account. I point out that this particular data **selection** procedure is theory-laden in that its

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In this paper, proposed the difficulty of data reduction for bug triage to decrease size of data and increase the quality of bug report. We use the technique of data preprocessing to reduce noise and redundancy in bug data and also predictive model used to find out the **order** applying the two algorithm already mentioned. The output of the predictive model reduction for bug triage is better than the data reduction technique. Bug triage motivates to assign an suitable programmer to fix a bug report. The reduced bug data consist of less bug reports and words compared with original bug data but given a similar information more than the original bug data.

Through a simulation study, we illustrate the implementation of these algorithms in the context of our spatial sample **selection** model. Our results show that the Bayesian estimator in all algorithms reports estimates that are close to the true parameter value for the autoregressive parameter of the **selection** equation. For the autoregressive parameter in the outcome equation, the deviation of the posterior mean estimate from the true parameter value is negligible for the Bayesian estimator in Algorithms 1–4. As for the parameters of the exogenous variables in the **selection** and outcome equations, the Bayesian estimator in Algorithms 1 and 4 performs relatively better in terms of reported deviations between the point estimates and the true parameter values. Our results also indicate that all algorithms have similar mixing properties under our priors specifications. For an empirical illustration, we use the application in the area of natural resource economics considered in Flores-Lagunes and Schnier, (2012) to model the spatial production within a fishery with a spatial sample **selection** model. Our Bayesian estimator reports much precise estimates for this application as it accounts for the full covariance structure implied by the spatial correlation.

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Another advantage of higher-**order** spectral analysis is that the cycle geometry of oscillations, such as asymmetry with respect to a horizontal axis (skewed oscillation) or with re- spect to a vertical axis (asymmetric oscillation) can be quan- tified using the biphase. A pure sine wave, for example, is neither skewed nor asymmetric, whereas a time series resem- bling a sawtooth is asymmetric. Skewed and asymmetric cy- cle geometry can identify, for example, abrupt climatic shifts, sudden shifts in the climate system that exceed the magnitude of the background variability (King, 1996). Abrupt climate shifts have occurred numerous times in the past and have dire impacts on ecological and economic systems (Alley et al., 2003). An understanding of past abrupt climate shifts is essential to understanding future climate change and so there is a need to quantify nonlinearities present in climatic oscil- lations.

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constructive parameters of the engine, especially the constructive angle. V-angle is very crucial factor as it immediate influence on the overall dynamics of the mechanism. An unbalance of forces is produced in rotary or reciprocating machinery due to the inertia forces associated with the moving masses. Balancing is the process of designing or modifying machinery so that the unbalance is reduced to an acceptable level and if possible is eliminated completely. The unbalance forces exerted on frame by the moving machine members are time varying, impart vibratory motion to frame and produce noise. Balancing of bodies is necessary to avoid noise and vibrations which could cause catastrophic failure of machinery. By the proper **selection** of firing **order** some V- angle gives more stability than other.

At the other extreme are theories of sophisticated or full intelligence traders. These theories come from the financial literature and deal with “dealers” or “maker makers,” who must make the market in the presence of randomly arriving market orders rather deal with “traders,” who submit both market and limit orders and have randomly arriving incentives. Full intelligence models diverge from less sophisticated models in that full intelligence traders, as in (Garman, 1976) and (Amihud & Mendelson, 1980), form (correct) beliefs about their expected future **order** flows and beliefs about the location of FCE prices and submit limit and market orders based on those beliefs. Neither (Garman, 1976) nor (Amihud & Mendelson, 1980) are explicit about how these beliefs are formed, merely stating that the stochastic structure of supply and demand arrivals is known to market makers. (Amihud & Mendelson, 1980) also derives that market makers’ offer prices depend on the level of dealer inventory.

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Dickson (1966) presented and listed 23 important evaluation criteria for supplier **selection**. (Wu et al., 2010; Haleh et al. (2011); Zhang et al., 2011) and many other researchers studied the supplier **selection** and presented models which can help industrial groups to choose the best suppliers and allocate quantity orders to each supplier because selecting the right supplier gives firms a competitive edge, reduced cost, improved quality, and reduces their lead time and decreases supply chain’s risk.

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Among the interesting open problems, we believe investigating the informa- tiveness of the projected samples obtained with PPs in the context of protected implementations is promising – it was essentially left out of our analysis so far. Different approaches could be considered for this purpose. One would be to re- fine the projection vectors, possibly based on an information theoretic objective function that would better reflect the resulting attacks’ data complexity. An alternative one would be to exploit non-linear projections, e.g. inspired by the “product combining” frequently used in second-**order** DPA [24, 32]. Yet, pre- liminary results suggest that non-linear projections may be hard(er) to exploit because the addition of non-informative samples when computing the objective function has higher impact on the (non-Gaussian) noise in this case. Besides, testing new objective functions that are cheap to compute and estimate, in the profiled and non-profiled settings, is another interesting research direction. Acknowledgements. F.-X. Standaert is a research associate of the Belgian Fund for Scientific Research (FNRS-F.R.S.). This work has been funded in parts by the European Commission through the ERC project 280141 (CRASH).

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An important feature of this study is that it has compared results from different machine learning algorithms on several data sets. This paper contributes new results to the framework of software defect prediction. The software prediction performance measures i.e. precision, recall, f- measure and accuracy are calculated on 4 different datasets. These measures have been calculated in two ways firstly by considering all the attributes of the dataset and secondly by attribute **selection** techniques. The results from these two techniques are compared and the best results are taken into consideration for prediction of faulty modules. The attribute **selection** techniques in some cases prove to be very efficient and hence improve the prediction performance measures.

The minimum of these three costs, i.e. 375, will be the value of the last row (“Min” row) of Table 6, which shows that the optimal ordering policy would be as follows: placing an **order** on supplier 2 to satisfy demands of periods 2 and 3, and placing an **order** on supplier 1 to satisfy demand of the first period. Similarly, for the fourth column, the minimum cost of 455 is the optimal ordering policy which suggests placing an **order** on supplier 1 in period 1 to satisfy the demand of this period, and placing an **order** on supplier 2 in period 2 to satisfy demands of periods 2 through 4. In Table 7, we have compared our proposed dynamic programming method with the branch and bound (B&B) method, which also yields the exact solution, in terms of CPU time (seconds). The first column of Table 7 is the problem size (no. of suppliers, no. of periods). Column 2 is the solution time of the B&B method, and column 3 is the solution time of proposed dynamic programming method. As it can be seen, for the small problem sizes, our method is considerably superior to the B&B method, and for larger problem sizes, while our method has reached the optimal solution in relatively small amount of time, the B&B method has failed to reach to any solution.

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The automatically estimated cluster numbers by F SGM M over 4 datasets are given in Table 6. The estimated cluster number was 2 ∼ 4 for “hard”, 3 ∼ 6 for “interest”, 3 ∼ 6 for “line”, and 2 ∼ 4 for “serve”. It is noted that the estimated cluster number was less than the number of ground truth classes in most cases. There are some reasons for this phenomenon. First, the data is not balanced, which may lead to that some important features can- not be retrieved. For example, the fourth sense of “serve”, and the sixth sense of “line”, their corre- sponding features are not up to the **selection** criteria. Second, some senses can not be distinguished using only bag-of-words information, and their difference lies in syntactic information held by features. For example, the third sense and the sixth sense of “in- terest” may be distinguished by syntactic relation of feature words, while the bag of feature words occur- ring in their context are similar. Third, some senses are determined by global topics, rather than local contexts. For example, according to global topics, it may be easier to distinguish the first and the second sense of “interest”.

Applying **selection** index method is reasonable in the Pannon large breed, especially for carcass traits. As a paternal crossing partner, the amount of the valuable meat parts has to be improved, thus more profi t can be obtained for the slaughterhouse. If traits are diffi cult to determine from an economic point of view the implementation of “desired gains” can be a good alternative to create a new breeding goal.