In order to improve the computational efficiency of conformal predictor, distance metric learning methods were used in the algorithm. The process of learning was divided into two stages: offline learning and online learning. Firstly, part of the training data was used in distance metric learning to get a space transformation matrix in the offline learning stage; Secondly, standard CP-KNN was co...

This paper proposes audio coding using an efficient long-term prediction method to enhance the perceptual quality of audio codecs to speech input signals at low bit-rates. The MPEG-4 AAC-LTP exploited a similar concept, but its improvement was not significant because of small prediction gain due to long prediction lags and aliased components caused by the transformation with a time-domain alias...

This article proposes a new classifier inspired on a biological immune systems’ characteristic. This immune based predictor also belongs to the class of k-nearest-neighbors algorithms. Nevertheless, its main features, compared to other artificial immune classifiers, are the assumption that training set is the antibodies’ population and a suppression mechanism that tries to reduce the training s...

Remote sensing technologies can accurately capture environmental characteristics, and together with environmental modeling approaches, help to predict climate-sensitive infectious disease outbreaks. Brucellosis remains rampant worldwide in both domesticated animals and humans. This study used human brucellosis (HB) as a test case to identify important environmental determinants of the disease a...

Let (X,Y ) be a random variable consisting of an observed feature vector X ∈ X and an unobserved class label Y ∈ {1, 2, . . . , L} with unknown joint distribution. In addition, let D be a training data set consisting of n completely observed independent copies of (X,Y ). Usual classification procedures provide point predictors (classifiers) Ŷ (X,D) of Y or estimate the conditional distribution ...

In this note we introduce a method for robust principal component regression. Robust principal components are computed from the predictor variables, and they are used afterwards for estimating a response variable by performing robust linear multiple regression. The performance of the method is evaluated at a test data set from geochemistry. Then it is used for the prediction of censored values ...

Whether the point of deflection from linearity of heart rate (HRD) coincides with ventilatory threshold (VT) has not been extensively examined in children. The purpose of this study was to assess the relationship between the VO, measured at VT and the VO, measured at HRD. Twenty-two boys with a mean age of 10.7 years ( f 1 .O) performed a graded exercise test to determine VT, HRD, and V0,max. T...

We describe the systems submitted by SRI International and the University of the Basque Country for the Semantic Textual Similarity (STS) SemEval-2012 task. Our systems focused on using a simple set of features, featuring a mix of semantic similarity resources, lexical match heuristics, and part of speech (POS) information. We also incorporate precision focused scores over lexical and POS infor...

This work aims at creating expressive voices from audiobooks using semantic selection. First, for each utterance of the audiobook an acoustic feature vector is extracted, including iVectors built on MFCC and on F0 basis. Then, the transcription is projected into a semantic vector space. A seed utterance is projected to the semantic vector space and the N nearest neighbors are selected. The sele...