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177
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 448 (52 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.
The use of the area under the ROC curve in the evaluation of machine learning algorithms
 Pattern Recognition
, 1997
"... AbstractIn this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Percept ..."
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Cited by 436 (0 self)
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AbstractIn this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multilayer Perceptron, kNearest Neighbours, and a Quadratic Discriminant Function) on six "real world " medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for "single number " evaluation of machine
Speaker recognition: A tutorial
"... A tutorial on the design and development of automatic speakerrecognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person’s claimed id ..."
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Cited by 160 (2 self)
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A tutorial on the design and development of automatic speakerrecognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person’s claimed identity. Speech processing and the basic components of automatic speakerrecognition systems are shown and design tradeoffs are discussed. Then, a new automatic speakerrecognition system is given. This recognizer performs with 98.9 % correct identification. Last, the performances of various systems are compared.
Texture classification by wavelet packet signatures
 IEEE Transaction PAMI
, 1993
"... This paper introduces a new approach tocharacterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classi cation of twenty ve natural textures. Both energy and entropy metrics were computed for each wavelet packet a ..."
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Cited by 156 (3 self)
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This paper introduces a new approach tocharacterize textures at multiple scales. The performance of wavelet packet spaces are measured in terms of sensitivity and selectivity for the classi cation of twenty ve natural textures. Both energy and entropy metrics were computed for each wavelet packet and incorporated into distinct scale space representations, where each wavelet packet (channel) re ected a speci c scale and orientation sensitivity. Wavelet packet representations for twenty ve natural textures were classi ed without error by a simple twolayer network classi er. An analyzing function of large regularity (D 20) was shown to be slightly more e cient inrepresentation and discrimination than a similar function with fewer vanishing moments (D6). In addition, energy representations computed from the standard wavelet decomposition alone (17 features) provided classi cation without error for the twenty ve textures included in our study. The reliability exhibited by texture signatures based on wavelet packets analysis suggest that the multiresolution properties of such transforms are bene cial for accomplishing segmentation, classication and subtle discrimination of texture. Index Terms{Feature extraction, texture analysis, texture classi cation, wavelet transform, wavelet packet, neural networks.
A System for Sound Analysis/Transformation/Synthesis Based on a Deterministic Plus Stochastic Decomposition
, 1989
"... This dissertation introduces a new analysis/synthesis method. It is designed to obtain musically useful intermediate representations for sound transformations. The method’s underlying model assumes that a sound is composed of a deterministic component plus a stochastic one. The deterministic compone ..."
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Cited by 102 (6 self)
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This dissertation introduces a new analysis/synthesis method. It is designed to obtain musically useful intermediate representations for sound transformations. The method’s underlying model assumes that a sound is composed of a deterministic component plus a stochastic one. The deterministic component is represented by a series of sinusoids that are described by amplitude and frequency functions. The stochastic component is represented by a series of magnitudespectrum envelopes that function as a timevarying filter excited by white noise. Together these representations make it possible for a synthesized sound to attain all the perceptual characteristics of the original sound. At the same time the representation is easily modified to create a wide variety of new sounds. This analysis/synthesis technique is based on the shorttime Fourier transform (STFT). From the set of spectra returned by the STFT, the relevant peaks of each spectrum are detected and used as breakpoints in a set of frequency trajectories. The deterministic signal is obtained by synthesizing a sinusoid from each trajectory. Then, in order to obtain the stochastic component, a set of spectra of the deterministic component is computed, and these spectra are subtracted from the spectra of the original sound. The resulting spectral residuals are approximated by a series of envelopes, from which the stochastic signal is generated by performing an inverseSTFT. The result is a method that is appropriate for the manipulation of sounds. The intermediate representation is very flexible and musically useful in that it offers unlimited possibilities for transformation. iii iv v To Eva and Octavi vi
An Empirical Comparison of Four Initialization Methods for the KMeans Algorithm
, 1999
"... In this paper, we aim to compare empirically four initialization methods for the KMeans algorithm: random, Forgy, MacQueen and Kaufman. Although this algorithm is known for its robustness, it is widely reported in literature that its performance depends upon two key points: initial clustering an ..."
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Cited by 95 (0 self)
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In this paper, we aim to compare empirically four initialization methods for the KMeans algorithm: random, Forgy, MacQueen and Kaufman. Although this algorithm is known for its robustness, it is widely reported in literature that its performance depends upon two key points: initial clustering and instance order. We conduct a series of experiments to draw up (in terms of mean, maximum, minimum and standard deviation) the probability distribution of the squareerror values of the final clusters returned by the KMeans algorithm independently on any initial clustering and on any instance order when each of the four initialization methods is used. The results of our experiments illustrate that the random and the Kaufman initialization methods outperform the rest of the compared methods as they make the KMeans more effective and more independent on initial clustering and on instance order. In addition, we compare the convergence speed of the KMeans algorithm when using each o...
Query By Image Example: The Candid Approach
, 1995
"... CANDID (Comparison Algorithm for Navigating Digital Image Databases) was developed to enable contentbased retrieval of digital imagery from large databases using a querybyexample methodology. A user provides an example image to the system, and images in the database that are similar to that exampl ..."
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Cited by 88 (1 self)
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CANDID (Comparison Algorithm for Navigating Digital Image Databases) was developed to enable contentbased retrieval of digital imagery from large databases using a querybyexample methodology. A user provides an example image to the system, and images in the database that are similar to that example are retrieved. The development of CANDID was inspired by the Ngram approach to document fingerprinting, where a "global signature" is computed for every document in a database and these signatures are compared to one another to determine the similarity between any two documents. CANDID computes a global signature for every image in a database, where the signature is derived from various image features such as localized texture, shape, or color information. A distance between probability density functions of feature vectors is then used to compare signatures. In this paper, we present CANDID and highlight two results from our current research: subtracting a "background" signature from ever...
Heterogeneous Learning in the Doppelgänger User Modeling System
 Interaction
, 1995
"... Doppelg anger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. Doppelg anger's learning is called heterogeneous for two reasons: first, multiple learning techniques are used to inter ..."
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Cited by 76 (0 self)
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Doppelg anger is a generalized user modeling system that gathers data about users, performs inferences upon the data, and makes the resulting information available to applications. Doppelg anger's learning is called heterogeneous for two reasons: first, multiple learning techniques are used to interpret the data, and second, the learning techniques must often grapple with disparate data types. These computations take place at geographically distributed sites, and make use of portable user models carried by individuals. This paper concentrates on Doppelg anger's learning techniques and their implementation in an applicationindependent, sensorindependent environment. Key words: User model, machine learning, serverclient architecture, multivariate statistical analysis, Markov models, Beta distribution, linear prediction. 1 Introduction When users interact with a computer, they provide a great deal of information about themselves. Even when they are not physically at a computer console,...
Fully constrained least squares linear mixture analysis for material quantification in hyperspectral imagery
 IEEE Trans. on Geoscience and Remote Sensing
, 2001
"... Abstract—Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMAbased estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed ..."
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Cited by 69 (4 self)
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Abstract—Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMAbased estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sumtoone constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of materials present in an image pixel to be one and the second imposes a constraint that these abundance fractions be nonnegative. While the first constraint is easy to deal with, the second constraint is difficult to implement since it results in a set of inequalities and can only be solved by numerical methods. Consequently, most LSMAbased methods are unconstrained and produce solutions that do not necessarily reflect the true abundance fractions of materials. In this
Gesture recognition: A survey
 IEEE TRANSACTIONS ON SYSTEMS, MAN AND CYBERNETICS  PART C
, 2007
"... Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging fr ..."
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Cited by 69 (0 self)
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Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finitestate machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted.