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A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, p...

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the dif...

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally t...

The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order...

A general non-parametric technique is proposed for the analysis of a complex multimodal feature space and to delineate arbitrarily shaped clusters in it. The basic computational module of the technique is an old pattern ...

In recent years there has been a growing interest in the study of sparse representation of signals. Using an overcomplete dictionary that contains prototype signal-atoms, signals are described by sparse linear combinatio...

A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used t...

Materias / palabras clave: Separable space; Feature (linguistics); Feature integration theory; Artificial intelligence; Computer science; Variety (cybernetics); Stimulus (psychology); Identification (biology); Pattern recognition (psychology); Cog...

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynam...

Materias / palabras clave: Computer science; Visualization; Functional magnetic resonance imaging; Software; Workstation; Magnetic resonance imaging; Transformation (genetics); Overlay; Artificial intelligence; Computer vision; Unix; Pattern recog...

Materias / palabras clave: Intraclass correlation; Reliability (semiconductor); Region of interest; Cerebral cortex; Computer science; Magnetic resonance imaging; Artificial intelligence; Pattern recognition (psychology); Cortex (anatomy); Human b...

Materias / palabras clave: Receiver operating characteristic; Computer science; Machine learning; Artificial intelligence; Simple (philosophy); Data mining; Pattern recognition (psychology); Philosophy; Epistemology

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construc...

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up ...

Materias / palabras clave: Computer science; Feature (linguistics); Block (permutation group theory); Convolutional neural network; Pattern recognition (psychology); Code (set theory); Artificial intelligence; Algorithm; Mathematics; Programming l...

Materias / palabras clave: Computer science; Deep learning; Artificial intelligence; Abstraction; Representation (politics); Layer (electronics); Object (grammar); Backpropagation; Convolutional neural network; Feature learning; Pattern recognitio...

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with...

Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In...

Materias / palabras clave: Convolutional neural network; Skin cancer; Artificial intelligence; Deep learning; Medicine; Computer science; Biopsy; Skin biopsy; Deep neural networks; Cancer; Pattern recognition (psychology); Dermatology

Functionality-specific vulnerabilities, which mainly occur in Application Programming Interfaces (APIs) with specific functionalities, are crucial for software developers to detect and avoid. When detecting individual fu...

Materias / palabras clave: Computer science; Pooling; Encoder; ENCODE; Artificial intelligence; Segmentation; Pyramid (geometry); Pattern recognition (psychology); Convolutional neural network; Upsampling; Separable space; Convolution (computer sc...

Materias / palabras clave: Robustness (evolution); Mathematics; Artificial intelligence; Computer science; Algorithm; Machine learning; Kernel (algebra); Decision tree; Tree (set theory); Pattern recognition (psychology); Mathematical analysis; Ch...

An automated method for segmenting magnetic resonance head images into brain and non-brain has been developed. It is very robust and accurate and has been tested on thousands of data sets from a wide variety of scanners ...

Feature pyramids are a basic component in recognition systems for detecting objects at different scales. But pyramid representations have been avoided in recent object detectors that are based on deep convolutional netwo...