The context-tree weighting algorithm 6] is an eecient universal source coding method for tree sources. Although a nite accuracy version of this algorithm has been analysed in 8], it is better to implement the algorithm as proposed in 7]. There it was suggested to store in each node s of the context tree, instead of an estimated probability P s e and a weighted probability P s w , the (logarithm...

We consider the matching function in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the similarity of the unknown speaker and the models in the database. In this paper, we propose to use weighted matching method that takes into account ...

We present the results of an experiment on extending the automatic method of Machine Translation evaluation BLUE with statistical weights for lexical items, such as tf.idf scores. We show that this extension gives additional information about evaluated texts; in particular it allows us to measure translation Adequacy, which, for statistical MT systems, is often overestimated by the baseline BLE...

A typical feedforward network has units arranged in a distinct layered topology. Units are connected to one another and each connection is associated with a real number, which is called the weight of the connection. During network training, the connection weights are adjusted in order to correctly classify the training data. The network weights are basically dependent on the training data set a...

Feature extraction is the important prerequisite of classifying text effectively and automatically. TF· IDF is widely used to express the text feature weight. But it has some problems. TF•IDF can’t reflect the distribution of terms in the text, and then can’t reflect the importance degree and the difference between categories. This paper proposes a new feature weighting method—TF•IDF•Ci to whic...

Semi-supervised learning, which uses a small amount of labeled data in conjunction with a large amount of unlabeled data for training, has recently attracted huge research attention due to the considerable improvement in learning accuracy. In this work, we focus on semisupervised variable weighting for clustering, which is a critical step in clustering as it is known that interesting clustering...

Class imbalance problem in data, plays a critical role in use of machine learning methods for text classification since feature selection methods expect homogeneous distribution as well as machine learning methods. This study investigates two different kinds of feature selection metrics (one-sided and two-sided) as a global component of term weighting schemes (called as tffs) in scenarios where...

For a graph G(V,E) (|V | = n) and a vertex s ∈ V , a weighting scheme (w : E → N) is called a min-unique (resp. max-unique) weighting scheme, if for any vertex v of the graph G, there is a unique path of minimum (resp. maximum) weight from s to v. Instead, if the number of paths of minimum (resp. maximum) weight is bounded by n for some constant c, then the weighting scheme is called a min-poly...

In text categorization, term weighting methods assign appropriate weights to the terms to improve the classification performance. In this study, we propose an effective term weighting scheme, i.e. tf.rf , and investigate several widely-used unsupervised and supervised term weighting methods on two popular data collections in combination with SVM and kNN algorithms. From our controlled experimen...

Abstract— We propose a design method for IIR low-pass differentiators under a specified maximum pole radius constraint. In the proposed method, we express the design problem in a quadratic form with respect to the coefficients of the transfer function. Since the cost function includes a weighting function, the frequency-weighting can be specified in the pass-band. Also, a linear phase property ...