On the other hand, most studies use categorical cross-entropy loss function, which is not optimal for the ordinal regression problem, to train the deep learning models. In this study, we propose a novel loss function called class distance weighted cross-entropy (CDW-CE) that respects the ...
prediction = lasagne.layers.get_output(network, deterministic=True) lossAll = lasagne.objectives.categorical_crossentropy(prediction, Y)#loss functionloss = lossAll.mean() loss = loss + l2_penalty accuracy = T.mean(T.eq(T.argmax(prediction, axis=1), Y), dtype=theano.config.floatX) ...
model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer=optm) earlystop = EarlyStopping(monitor='val_loss', patience=1, verbose=1) model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, validation_split=0.1, show_accuracy=True, callbacks=...
, K} is a categorical variable that indicates the community to which node i was assigned, γ is the structural resolution parameter, and δ(zi, zj) is the Kronecker delta function, which evaluates to 1 when zi = zj and 0 otherwise. In short, modularity maximization seeks to ...
The macroscale connectome is the network of physical, white-matter tracts between brain areas. The connections are generally weighted and their values interpreted as measures of communication efficacy. In most applications, weights are either assigned ba
14 Microaggregation for categorical variables: a median based approach Torra, V [121] PSDP Conference on privacy in statistical databases (PSD 2004) 56 2004 15 Soft computing-based aggregation methods for human resource management Canos, L; Liern, V [122] EJOR 3rd Biannual conference on operat...
Our work therefore complements ongoing efforts to extend traditionally categorical distinctions to continuous measurements in the context of core-periphery structure53 and community structure54. Pragmatic Utility in Neuronal and Brain Networks. Network statistics that are independent of density are ...
Existing distance-based outlier mining methods do not consider the impact of each attribute's importance degree, thereby resulting in poor mining accuracies. To address this problem, we propose a n...
In SNUBH, the Consortium to Establish a Registry for Alzheimer’s Disease Korean version was used, with standard deviations (SDs) greater than − 1.5 for the age-, sex-, and education-adjusted norms on ten neuropsychological tests (i.e., Categorical Fluency Test, modified Boston Naming...
These selected factors involve both continuous and categorical data, of which the former may increase the computational amount much and subsequently lead to complex data processing. To remedy this, these factors were discretized into several categories with the same attribute interval (Figure 7). A ...