A well known difficulty in estimating conditional moment restrictions is that the parameters of interest need not be globally identified by the implied unconditional moments. In this paper, we propose an approach to constructing a continuum of unconditional moments that can ensure parameter identifiability. These unconditional moments depend on the “instruments” generated from a “generically co...

Usually neuro-fuzzy networks are trained by using mean squared error (MSE) as cost function. This approach may lead to some problems when the network is used to classify patterns. In this paper, we propose a new empirical risk functional that is particularly suitable for classification tasks, when neuro-fuzzy learning is based on a gradient descent strategy. This functional has the properties o...

When the goal is to achieve the best correct classification rate, cross entropy and mean squared error are typical cost functions used to optimize classifier performance. However, for many real-world classification problems, the ROC curve is a more meaningful performance measure. We demonstrate that minimizing cross entropy or mean squared error does not necessarily maximize the area under the ...

This version includes some results on Besov spaces at the end! 1 The single sequence problem Consider the estimation of a sequence of means μ = (μ1, μ2, . . . , μn) given independent observations Xi ∼ N(μi, 1). Define ŵ to be the marginal maximum likelihood estimator of the parameter w based on X1, X2, . . . , Xn, defined subject to the constraint that the corresponding threshold t̂ of the poste...

This two-part paper presents a feedback-based crosslayer framework for distributed sensing and estimation of a dynamical process in a wireless sensor network (WSN) in which the sensor nodes (SNs) communicate their measurements to a fusion center (FC) via B orthogonal wireless channels. Cross-layer factors such as packet collisions and the sensingtransmission costs are accounted for. Each SN ada...

In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types, and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. W...

In applied forecasting, there is a trade-off between in-sample fit and out-ofsample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, o...

We propose a new cost function for neural network classification: the error density at the origin. This method provides a simple objective function that can be easily plugged in the usual backpropagation algorithm, giving a simple and efficient learning scheme. Experimental work shows the effectiveness and superiority of the proposed method when compared to the usual mean square error criteria ...

To overcome the shortcomings of low transmission rate due to limited bandwidth in traditional wired transmission, this paper proposes a wireless downhole transmission system based on optical OFDM technique. This system is advantageous in both optical communications technique and OFDM technique. It has higher transmission rate. Furthermore, in order to overcome the defects of being sensitive to ...

Recommender systems elicit the interests and preferences of individuals and make recommendations accordingly, a main challenge for expert and intelligent systems. An essential problem in recommender systems is to learn users’ preference dynamics, that is, the constant evolution of the explicit or the implicit information, which is diversified throughout time according to the user actions. Also,...