This paper investigates perceptron training for a wide-coverage CCG parser and compares the perceptron with a log-linear model. The CCG parser uses a phrase-structure parsing model and dynamic programming in the form of the Viterbi algorithm to find the highest scoring derivation. The difficulty in using the perceptron for a phrase-structure parsing model is the need for an efficient decoder. W...

Perceptron is a classic online algorithm for learning a classification function. In this paper, we provide a novel extension of the perceptron algorithm to the learning to rank problem in information retrieval. We consider popular listwise performance measures such as Normalized Discounted Cumulative Gain (NDCG) and Average Precision (AP). A modern perspective on perceptron for classification i...

One of the fundamental problems in learning is identifying members of two diierent classes. For example, to diagnose cancer, one must learn to discriminate between benign and malignant tumors. Through examination of tumors with previously determined diagnosis, one learns some function for distinguishing the benign and malignant tumors. Then the acquired knowledge is used to diagnose new tumors....

For improving the system performance of mobile Internet, how to provide the Quality of Experience (QoE) guarantee is an important factor. First, based on artificial neural network and adaptive cross-layer perceptron, we studied the cloudassisted QoE guarantee mechanism. Then, according to the power, we divided the distance and perceptron layers of mobile Internet and cloud into three levels. We...

This paper describes the application of the perceptron algorithm to the morphological disambiguation of Turkish text. Turkish has a productive derivational morphology. Due to the ambiguity caused by complex morphology, a word may have multiple morphological parses, each with a different stem or sequence of morphemes. The methodology employed is based on ranking with perceptron algorithm which h...

In this paper we present our teamwork main results in the area of the automatic speech recognition and understanding in Romanian language using hidden Markov models (HMM), artificial neural networks (multilayer perceptron, support vector machines, Kohonen networks) and hybrid models (fuzzy HMM, fuzzy multilayer perceptron, HMM/multilayer perceptron) for different small Romanian language corpora...

In this study, we propose a multi-layer perceptron with a glial network which is inspired from the features of glias in the brain. All glias in the proposed network generate independent oscillations, and the oscillations propagate through the glial network with attenuation. We apply the proposed network to the two-spiral problem. Computer simulations show that the proposed network gains a bette...

By recourse to a method based on information theory, we have studied the generalization problem in perceptrons. We considered different a priori distributions about the weights of the teacher perceptron. Our approach allows us to define the information gain from the examples used in the training procedure. The information gain can be used to choose a convenient example set for training the perc...

Previous works have shown that neural branch prediction techniques achieve far lower misprediction rate than traditional approaches. We propose a neural predictor based on two perceptron networks: the Combined Perceptron Branch Predictor. The predictor consists of two concurrent perceptron-like neural networks; one using as inputs branch history information, the other one program counter bits. ...

Perceptron learning bounds with real weights have been presented by several authors. In the present paper we study the perceptron learning task when using integer weights in [−k, k]. We present a sample complexity formula based on an exact counting result of the finite class of functions implemented by the perceptron, and show that this bound is less pessimistic than existing bounds for the dis...