In multi-label text classification, one text can be associated with multiple labels (label co- occurrence) (Zhang and Zhou, 2014). Since la- bel co-occurrence itself contains information, we would like to leverage the label co-occurrence to im- prove multi-label classification using a neural net- work (NN). We propose a novel NN initialization method that treats some of the neurons in the **final** **hidden** **layer** as dedicated neurons for each pattern of label co-occurrence. These dedicated neurons are initialized to connect to the corresponding co- occurring labels with stronger weights than to oth- ers. While initialization of an NN is an important research topic (Glorot and Bengio, 2010; Sutskever et al., 2013; Le et al., 2015), to the best of our knowl- edge, there has been no attempt to leverage label co- occurrence for NN initialization.

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The number of units in the **final** **hidden** **layer** can exceed the number of label co-occurrences in the training data. We must therefore decide what to do with the remaining **hidden** units. Kurata et al. (2016) assign random values to these units (shown in Figure 3 (B)). We will also use this scheme, but in addition we propose another variant: we assign the value zero for these neurons, so that the hid- den **layer** will only be initialized with nodes that represent label co-occurrence.

Many tasks in the biomedical domain re- quire the assignment of one or more pre- defined labels to input text, where the la- bels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary clas- sifier is trained for each label in the tax- onomy or ontology where all instances not belonging to the class are considered nega- tive examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the addi- tional computational cost of training par- allel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model **final** **hidden** **layer** such that it leverages label co-occurrence relations such as hypernymy. This approach ele- gantly lends itself to hierarchical classifi- cation. We evaluated this approach using two hierarchical multi-label text classifica- tion tasks in the biomedical domain using both sentence- and document-level classi- fication. Our evaluation shows promising results for this approach.

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With reference to the used literature [12], [13] is a neural network with one **hidden** **layer** (3 **layer** neural network) and a sufficient number of **hidden** neurons, capable of simulating each binary or continuous function with desired accuracy. In our case, we assume nine input neurons and two output neurons. Number of neurons in input **layer** is given by number of parameters for describing the communication. We are using neural network for classification to two groups. There can be only one neuron in output **layer**. But for software realization reasons we have to use two output neurons. Other parameters of neural network are next.

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A SAE model is created by stacking autoencoders to form a deep network by taking the output of the autoencoder found on the **layer** below as the input of the current **layer** .The l- layers in SAE, the first **layer** is trained as an autoencoder, with the training set as inputs. After obtaining the first **hidden** **layer**, the output of the kth **hidden** **layer** is used as the input of the (k + 1)th **hidden** **layer**, multiple autoencoders can be stacked hierarchically. This is shows in Fig. 2 By using the SAE network for traffic flow prediction, we need to add a standard predictor on the top **layer**. In this paper, we put a logistic regression **layer** on top of the network for supervised traffic flow prediction. The SAEs plus the predictor comprise the whole deep architecture model for traffic flow prediction.

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The ELM is fast in classification tasks and also generates a high performance generalization compared with most of the existing methods such as backpropagation (BP) networks and support vector machine (SVM), reported in [14]. Moreover, the experimental results in [14] show that the standard deviations of the results obtained by the ELM algorithm are less than those of other methods. Here, our procedure for training the networks is the same as the theory of the ELM algorithm with a strong emphasis on attribute weighting and the **hidden** weights of the SLFNs.

data completely unknown to them; this simulates the sit- uations in which ANNs would be used in climate models (where grid points play the role of stations). To test this, we choose the NL-Cab station for validation and DE-Keh as the unknown station. We selected these two stations be- cause the MOST method performed best for these stations; therefore it is a strong challenge for the ANNs to produce equivalent results. The results of the networks that perform best on the validation set are summarised in Table 4, where we compare the ANNs according to the increasing complex- ity of their network architecture. For comparison, and in view of reducing CPU time, we also show the results of the best simple networks (as defined in Sect. 2.6) in this table. Ta- ble 4 shows that all ANNs perform better than the MOST method on the validation data set (NL-Cab), in terms of the MSE and correlation coefficient (r). Applying these ANNs to the test data set (DE-Keh) results in an increased MSE and a lower correlation coefficient, whereas the MOST method performs better on the test data set. Among the ANNs, the 6–5–3–2 ANN displayed the best test performance with a MSE of 0.68×10 −2 , but the simpler 6–3–2 ANN was second best (also in terms of the MSE); thus, simple networks can be almost as good as larger networks. Networks with seven inputs have no substantial advantage over networks with six inputs in our research. ANNs with two **hidden** layers perform slightly better on the test data than ANNs with a single hid- den **layer**. The overall correlation between network outputs and target values is quite high (r ≥ 0.85) in all cases.

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In this section I present the neural network model which is used for the prediction of retrofitting/reconditioning/upgradation cost of CNC machines. I used multilayer neural network with either two **hidden** **layer** h1 and h2 or single **hidden** **layer** h1, number of neurons in the layers h1 and h2 will be determined by the Training performance of the network. Activation Functions used for **layer** h1 is ‘linear function’ and for **layer** h2 is ‘tan sigmoid’.

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As mentioned in the preceding chapter, the configuration and training of neural networks is a trail- and-error process due to such undetermined parameters as the number of nodes in the **hidden** **layer**, the learning parameter, and the number of training patterns. Hence, the I-section of 2.5 mm thickness is chosen so as to obtain the experience to configure and train the neural network. The parameters that are used to produce the training data are shown in Table 5.1. Moreover, Young’s Modulus is 250000 N/mm P

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The ANN always consists of atleast three layers: input **layer**, **hidden** **layer** and output **layer**. Each **layer** consists neurons, and each neuron is connected to the next **layer** through weights. Neurons in the input **layer** will send its output as input for neurons in the **hidden** **layer** and similar is the connection between **hidden** and output **layer**. Number of **hidden** layers and number of neurons in the **hidden** **layer** change according to the problem to be solved. The number of input and output neuron is same as the number of input and output variables. [2]

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Finally, the six-layered DAG-RNN architectures used to process 2D contact maps may shed some broader light on neural-style computations in multi-layered systems, including their distant biological relatives. First, preferential directions of propagation can be used in each **hidden** **layer** to integrate context along multiple cardinal directions. Second, the computation of each visible output requires the computation of all **hidden** outputs within the corresponding column. Thus **final** output converges to correct value first in the center of an output sheet, and then progressively propagates towards its boundaries. Third, weight sharing is unlikely to be exact in a physical implementa- tion and the effect of its fluctuations ought to be investigated. In particular, additional, but locally limited, degrees of freedom may provide increased flexibility without substantially increasing the risk of overfitting. Finally, in the 2D DAG-RNN architectures, lateral propagation is massive. This stands in sharp contrast with conventional connectionist architectures, where the primary focus has remained on the feedforward and sometimes feedback pathways, and lateral propagation used for mere lateral inhibition or “winner-take-all” operations.

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Another application of genetic algorithm is to search for optimal **hidden** **layer** architectures, connectivity, and training parameters for ANN for predicting community acquired pneumonia among patients with respiratory complaints. Feed- forward ANN that uses back propagation algorithm with 35 nodes in the input **layer**, one node in the output **layer**, and between 0 and 15 nodes in each of 0, 1, or 2 **hidden** layers, is determined by the developed genetic algorithm. Neural network structure and training parameters are represented by haploid chromosomes consisting of ‘‘genes’’ of binary numbers. Each chromosome has five genes. The first two genes are 4-bit binary numbers, representing the number of nodes in the first and second **hidden** layers of the network, each of which could range from 0 to 15 nodes. The third and fourth genes are 2-bit binary numbers, representing the learning rate and momentum with which the network has been trained, which each could assume discrete values of 0.01, 0.05, 0.1, or 0.5. The fifth gene is a 1-bit binary number, representing whether implicit within-**layer** connectivity using the competition algorithm [16].

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As one of the approaches to improve these problems, cascade-correlation learning algorithm was developed by Fahlman and Lebiere (1991) [16] and showed significant improvements. Cascade-correlation is a method of incre- mentally adding processing elements. Instead of adjusting the weights in an ANN of fixed topology, cascade-corre- lation begins with a minimal network, then automatically trains and adds new **hidden** units one by one, creating a multi-**layer** structure. Once a new **hidden** unit has been added to the ANN, its input-side weights are frozen. This unit then becomes a permanent feature-detector in the ANN, available for producing outputs or for creating other, more complex feature detectors. NeuralWorks Predict (NWP) software (NeuralWare Inc., Pittsburgh, PA, USA) was used in this study which implements the cascade- correlation learning algorithm. NWP outperforms other neural network tools in that it also builds ANNs in the clever strategy of stopping rules against over-fitting on empirical data. Moreover, NWP undertakes some nonlinear transformation for input variables, and produces input neurons for each transformation in advance of learning process to avoid the complex representation of the model. Types of transformation used include linear (scaling), log, log–log, exponential, exponential of exponent, square-root, square, inverse, inverse of square-root, inverse of square, and so on, depending on the complexity of the problem [17]. NWP also uses a genetic algorithm to make a suitable choice of input variables from the set of all input variables and transformations of input variables [17], since it effi- ciently explores the large space of subsets of possible input variables.

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The number of neurons in the **hidden** **layer** was determined by experiments comparing the network performances with a different number of neurons in the **hidden** **layer**. During the experiment, networks were tested with two to seven neurons in the **hidden** **layer**, and for every topology several trainings with the same training set were performed so that the performances of every topology could be estimated as objectively as possible. Networks with a small number of neurons (two and three neurons) in the **hidden** **layer** did not present satisfactory results, which can be attributed to an insufficiently rich network structure that implied a small capacity for the function approximation. Networks with five or more neurons in the **hidden** **layer** successfully approximated the input-output dependence, so any of those topologies was appropriate for implementation. In selecting the **final** topology, a general direction was used, saying that the total number of neurons in the neural network should be as small as possible, since in that way the generalization network abilities were increasing and the appearance of "over fitting" was avoided. Considering all the above mentioned, a network with five neurons in the **hidden** **layer** was selected for the **final** network structure.

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The capabilities to the single **layer** perceptron are limited to linear decision boundaries and simple logic functions. However, by cascading perceptrons in layers, we can implement complex decision boundaries and arbitrary Boolean expressions. Perceptrons in the network are called neurons or nodes and differs from Rosenbelt perceptron in the activation function used. The output of this **layer** feed into each of the second layered perceptron and so an. Often nodes are fully connected between layers i.e. every node in first **layer** is connected to every node in next **layer**. Refer figure 1.10 the multiple nodes in the output **layer** typically corresponds to multiple classes for the multiclass pattern recognition problem.

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An iterative self-constructing clustering algorithm is used to determine the number of hidden nodes in the hidden layer.. Data are described by clusters with appropriate centers and devi[r]

rotational speed increases the general trend is for the value of Q to increase also, and in . general the smaller network, 20 hidden neurons in the hidden layer, trained [r]

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The second experiment is done using the Artificial Neural Network with OneHidden Unit and default ten neurons in the **hidden** **layer** with the Bayesian regulation back propagation. trainbr is a network training function that updates the weight and bias values according to Levenberg-Marquardt optimization. [8] It minimizes a combination ofsquared errors and weights, and then determines the correct combination so as to producea network that generalizes well.

FEEDFOREWARD NEURAL NETWORK The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the **hidden** nodes (if any) and to the output nodes. There are no cycles or loops in the network. In the present work we are using Feedforeward neural network with backpropagation algorithm. We are employing LM algorithm as the backward propagation algorithm in the present work.

these are connected in a definite manner as like **layer** structure is called as neural network architecture. It is mostly widely used for the purpose of optimizing issues. It can have multiple numbers of layers of processing units in form of feed forward way. Neural network is used as predictor that computes the formal model parameters and discover the process itself. Also have to note that the Back-error propagation is commonly used neural network and it is being used effectively in application studies in wide range of region.