The next step in assembling these terms in the actual search is undertaken manually in an iterative **trial**-and- **error** **approach**. Because SRs usually aim to apply highly sensitive search strategies, the strategy should capture all references from the development set with sufficient preci- sion to prevent the retrieval of too many irrelevant refer- ences. During the course of an IQWiG project, the search strategy may be adjusted in consultation with the project team: for example, if a high sensitivity results in an exces- sive number of hits, a more precise strategy may be required. The results of the textual analysis are drawn upon to enable an informed and transparent decision regarding a change in strategy.

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Nevertheless, the performances of ANN system highly depend on training. In a typical ANN architecture, there are several parameters that need to be defined, i.e. number of neurons in a hidden layer, number of hidden layer, activation function of each neuron and also the training algorithms. Many experiments and tests need to be conducted to find the best architecture with optimized performance. Instead of **trial** and **error** **approach**, the study presents a technique called factorial design to investigate the effect of each parameter in ANN model and minimize the amount of tests that need to be conducted to determine the best ANN architecture.

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Electro hydraulic actuator system with unknown parameter as uncertainties is chosen as a numerical example in this study since its position tracking is highly nonlinear. Adaptive backstepping controller is designed for the system. The designed controller consists of several control and adaptation gain parameters. The number of the defined state of the system determines the number of control parameters and the number of virtual control input. In order to achieve desired response, suitable value of these parameters needs to be chosen carefully so that tracking **error** obtained will be smaller. Instead of using **trial** and **error** **approach** to find appropriate values of these parameters, Gravitational Search Algorithm (GSA) technique is used in this research work, so that system output tracked reference input given properly with smaller or zero tracking **error**. These can be seen in each graph obtained. These parameters value are varying depends on the requirement of operation of the system. Robustness of the designed controller with GSA technique also had been proved by varying the reference input injected to the system. Although different type of input is injected to the system, the system output tracked reference input smoothly with small tracking **error**. GSA technique used in this work also improves the tracking performance of the designed controller by finding correct value for all control parameters. Small value of Sum of Squared **Error** (SSE) yielded by this optimization technique proves outstanding performance of the combined adaptive backstepping controller with GSA technique.

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many real world applications that can be categorized by classification problems such as weather forecast, credit risk evaluation, medical diagnosis, bankruptcy prediction, speech recognition, handwritten character recognition [17]. The training of neural network is a complex task in this field of research. The main difficulty for adopting ANN is to find the appropriate combination of learning, transfer and training functions for the classification. In this paper, the performances of the four transfer functions (log sigmoid, tan sigmoid, pure linear and hard limit) in FFBPNN with **trial** and **error** **approach** will be determined to find out the best activation function for identifying the output classification responses of this network with two input and four output features (classes). 2. Methodology

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We propose a novel word embedding pre- training **approach** that exploits writing er- rors in learners’ scripts. We compare our method to previous models that tune the embeddings based on script scores and the discrimination between correct and corrupt word contexts in addition to the generic commonly-used embeddings pre-trained on large corpora. The com- parison is achieved by using the afore- mentioned models to bootstrap a neural network that learns to predict a holistic score for scripts. Furthermore, we in- vestigate augmenting our model with er- ror corrections and monitor the impact on performance. Our results show that our **error**-oriented **approach** outperforms other comparable ones which is further demon- strated when training on more data. Addi- tionally, extending the model with correc- tions provides further performance gains when data sparsity is an issue.

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Table 3: a sample of **error** detection rules The “modal_infinitive” rule is complex and it is described using 7 sub-instances, which share an identical label. Line 1 of the configuration excerpt contains three pairs as opposed to the other sub-instances. This does not contradict the combinational logic paradigm, since we can con- sider this rule as having a fixed input size of three and, as a result of logic minimization, the third parameter for 6 of the seven instances falls into the “DON’T CARE” special input class. The first i k r k pair (“s must”) is used to check if the

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One of the criticisms on this new stochastic **approach** of Clements and Izan (1987) was on the restriction of homoscedasticity on the variance of the **error** term in the OLS regression (Diewert, 1995). Crompton (2000) also pointed out this deficiency and extended the new stochastic **approach** to derive robust standard errors for the rate of inflation by relaxing the earlier restriction on the variance of the **error** term by considering an unknown form of heteroscedasticity. Selvanathan (2003) presented some comments and corrections on Crompton’s work. Selvanathan and Selvanathan (2004) showed how recent developments in stochastic **approach** to index number can be used to model the commodities prices in the OECD countries. Selvanathan and Selvanathan (2006) calculated annual rate of inflation for Australia, UK and US using stochastic **approach** 1 . These studies provided mechanism for calculating standard **error** for inflation. Rather than targeting the headline (YoY) inflation, some countries track 12 month moving average inflation as goal of monetary policy. However, there is no work in the literature to estimate standard **error** of period average inflation. We contribute by developing a mechanism to estimate standard **error** of period average inflation.

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Naturally one would aim to choose these parameters in such a way as to optimise the generalisation **error** of the SVM. Sometimes the parameters can be estimated by an expert user, but often this is not the case. In such cases other approaches to determining the parameters are needed. While the straightforward **approach** of doing a simple grid-search, wherein a range is set for each parameter and each combination of parameters are subsequently tested, is still popular a number of better approaches have been proposed. The area is of great interest as grid-search is intractable if the number of parameters are any more than two. Some of the possible extensions include the use of GAs (Rojas and Fernandez-Reyes, 2005; Fr¨ olich et al., 2003; Liu et al., 2005) and the calculation of the inter-cluster distance in the feature space (Wu and Wang, 2009). Chapelle proposes a gradient descent methods using various bounds of the generalisation **error** with respect to the kernel parameters (Chapelle et al., 2002) and shows very promising results as an automatic way of selecting the parameters. Hence a number of different approaches exists and their usefulness depends to some extent on the particular application. Grid search is therefore still widely used in current literature.

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Abstract: Function approximation is an important type of supervised machine learning techniques, which aims to create a model for an unknown function to find a relationship between input and output data. The aim of the proposed **approach** is to develop and evaluate a function approximation models using Radial Basis Function Neural Networks (RBFN) and Particles Swarm Optimization (PSO) algorithm. We proposed Hybrid RBFN with PSO (HRBFN-PSO) **approach**, the proposed **approach** use PSO algorithm to optimize the RBFN parameters, depending to the evolutionary heuristic search process of PSO, here PSO use to optimize the best position of the RBFNN centers c, the weights w optimize using Singular Value Decomposition (SVD) algorithm and the Radii r optimize using K-Nearest Neighbors (Knn) algorithm, within the PSO iterative process, which means in each iterative process of PSO, the weights and Radii are updated depending the fitness (**error**) function. The experiments are conducted on three nonlinear benchmark mathematical functions. The results obtained on the training data clarify that HRBFN-PSO **approach** improved the approximation accuracy than other traditional approaches. Also, this result shows that HRBFN-PSO reduces the root mean square **error** and sum square **error** dramatically compared with other approaches.

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Balancing habitual and deliberate forms of choice entails a comparison of their respective merits—the former being faster but inflexible, and the latter slower but more versatile. Here, we show that arbitration between these two forms of control can be derived from first principles within an Active Inference scheme. We illustrate our arguments with simulations that reproduce rodent spatial decisions in T-mazes. In this context, deliberation has been associated with vicarious **trial** and **error** (VTE) behavior (i.e., the fact that rodents sometimes stop at decision points as if deliberating between choice alternatives), whose neurophysiological correlates are “forward sweeps” of hippocampal place cells in the arms of the maze under con- sideration. Crucially, forward sweeps arise early in learning and disappear shortly after, marking a transition from delib- erative to habitual choice. Our simulations show that this transition emerges as the optimal solution to the trade-off between policies that maximize reward or extrinsic value (habitual policies) and those that also consider the epistemic value of exploratory behavior (deliberative or epistemic policies)—the latter requiring VTE and the retrieval of episodic information via forward sweeps. We thus offer a novel perspective on the optimality principles that engender forward sweeps and VTE, and on their role on deliberate choice.

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As it is visible in the previous table concrete projects that lead to tangible results constitute one of most important elements when accomplishing municipal territorial marketing. The same applies to the communication with citizens, entrepreneurs and further target groups residing in the municipality. On the contrary, more general concepts, such as marketing as a philosophy of municipal development, are rather the matter of the future. This reveals certain fragmentariness and spontaneity when implementing the marketing at the municipal level. This process is unfortunately often of **trial** – and – **error** character.

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signments of error, including the fact that: "the trial court committed prejudicial error by granting immunity to a witness who had no claim of Fifth Amendment privilege because her self[r]

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The existing approach for correcting the spelling error in the languages that have no word boundary assumes that all substrings in input sentence are error strings, and then tries to cor[r]

No doubt that the persistent rise in the price levels of commodities and services adversely affects the economic performance. The goal of each and every Government is to maintain low and relatively stable levels of inflation. Creeping or mild inflation can be viewed as having favorable impacts on the economy; on the other hand zero inflation is harmful to other sectors in the economy. The right level of inflation, is somewhere in the middle. The study analyzed the major determinants of inflation in India extracting 54 time series quarterly observations. The study employed Johansen-juselius cointegration methodology to test for the existence of a long run relationship between the variables. The cointegrating regression so far considers only the long-run property of the model, and does not deal with the short-run dynamics explicitly. For this, the **error** correction from the long run determinants of inflation is then used as a dynamic model to estimate the short run determinants of inflation. The study concluded that the GDP and broad money have a positive effect on the inflation in long run. On the other hand, interest rate and exchange rate has a negative effect. The income coefficient is 0.37 and showing significant, implying that in India, a one percent increase in income while others keep constant contributes 0.37% increase in inflation. Similarly the money coefficient is 0.047 and showing significant, implying that in India, one percent increase in money supply leads to a 5% increase in price level.

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participants occurred. The format of the interviews followed focus group protocol. The focus groups were used to gather opinions and thought processes of the participants. There were two focus group interviews, one for the **trial** and **error** group and one for the knowledge application learning group. The interviews were audio-recorded, transcribed, and coded. The researcher used these interviews to attempt to gain a certain perspective from each particular group (Krueger, 2009). Focus group interviews are well suited and a viable source for qualitative studies including grounded theory (Webb & Kerven, 2001). The purpose of the interview was to gain knowledge of the reasoning behind each student’s choices made. The interview gave a deeper insight to the rationale of each student’s selection when creating his or her virtual simulations. This rationale and reasoning furthered the knowledge gained from the quantitative data analysis and added logic and an explanation to why the students made each choice they did and created each iteration. The rational choice theory indicates an individual will make the best choice for his or her own interest (Scott, 2000). The interview questions helped to determine the student’s line of thinking in relation to their own costs and benefits on the glider simulation activity. Markers were used to connect and relate the interviews better to the Rational Choice Theory.

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Errors produced by the GPS system affect in the same way the receivers located near each other in a limited radius (Grewal et al. 2007). This implies that errors are strongly correlated among near receivers. Thus, if the **error** produced in one receiver is known, it can be spread towards the rest in order to make them correct their position. This principle is only applicable to receivers that are exactly the same; since, if different, their specifications change so the signal processed by one individual is not the same to that processed by another one. All GPS differential methods use the same concept (Di Lecce et al. 2008). DGPS requires a base station with a GPS receiver in a precise known position. The base compares its known position with that calculated by the satellite signal. The estimated difference in the base is applicable then to the mobile GPS receiver as a differential correction with the premise that any two receivers relatively near experiment similar errors (Zandbergen & Arnold 2011).

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Bouchut and Perthame [3]), and are known as the Kuznetsov techniques (see Kuznetsov [11], and Kuznetsov and Volshin [12]). The new thing here is the generalization of these techniques to the kinetic **approach** of Perthame and Tadmor, as the approximate solution is obtained from the kinetic model. We prepare by the lemma.

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respectively. The empirical MEE is implemented by minimizing these computable quantities. Though the MEE principle has been proposed for a decade and MEE algorithms have been shown to be effective in various applications, its theoretical foundation for mathematical **error** anal- ysis is not well understood yet. There is even no consistency result in the literature. It has been observed in applications that the scaling parameter h should be large enough for MEE algorithms to work well before smaller values are tuned. However, it is well known that the convergence of Parzen windowing requires h to converge to 0. We believe this contradiction imposes difficulty for rigorous mathematical analysis of MEE algorithms. Another technical barrier for mathematical analysis of MEE algorithms for regression is the possibility that the regression function may not be a minimizer of the associated generalization **error**, as described in detail in Section 3 below. The main contribu- tion of this paper is a consistency result for an MEE algorithm for regression. It does require h to be large and explains the effectiveness of the MEE principle in applications.

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In sample size calculations, it is more common to work in terms of the power of a clinical **trial**. Power is one minus the probability of a type II **error**, thus giving the probability of correctly rejecting the null hypothesis. Therefore, power is usually set to be between 80% and 90%. The minimum power should be 80%. Because of the practical nature of conducting a clinical **trial**, it is recom- mended to set the power as high as possible, preferably at least 90%. When designing a **trial**, it is necessary to estimate the population standard deviation and study completion rates. These are just estimates however, and once the **trial** has started, you may find them to be awry. If the standard deviation is higher and the completion rate lower than anticipated, the sample size might need to be increased to maintain the desired power. However, this can have logistical impacts on the conduct of the **trial**: increased budgets, timeline extensions and protocol amendments. If the study was designed with 90% power, then a deci- sion could be made to forego a little power to maintain the same budget and timelines. If this study was designed with 80% power, there may not be this option as the study is already at its minimum power. A 90% powered study is less sensitive to the assumptions in the sample size calculation than a 80% powered study.

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Entropic quantities are another means of comparing two probability distributions, an **approach** taken recently by Buscemi et al. [ 33 ] and Coles and Furrer [ 35 ] (see also Martens and de Muynck [ 29 ] ). Both contributions formalize **error** and disturbance in terms of relative or conditional entropies, and derive their results from entropic uncertainty relations for state preparation which incorporate the effects of quantum entanglement [ 6, 44 ] . They differ in the choice of the entropic measure and the choice of the state on which the entropic terms are evaluated. Buscemi et al. find state-independent **error**-disturbance relations involving the von Neumann entropy, evaluated for input states which describe observable eigenstates chosen uniformly at random. As described in Sec. 6, the restriction to uniformly-random inputs is significant, and leads to a characterization of the average-case behavior of the device (averaged over the choice of input state), not the worst-case behavior as presented here. Meanwhile, Coles and Furrer make use of general Rényi-type entropies, hence also capturing the worst-case behavior. However, they are after a state-dependent **error**- disturbance relation which relates the amount of information a measurement device can extract from a state about the results of a future measurement of one observable to the amount of disturbance caused to other observable.

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