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Fast and Accurate Determination of the Margin of Normalised Difference for Classification – This paper investigates the potential value of the concept of the marginal metric in classification. It describes a novel task in a text-based task-based optimization system to learn a latent metric for classification. We propose a novel technique based on the idea […]

An Adaptive Aggregated Convex Approximation for Log-Linear Models – In this paper, a novel method for estimating a matrix $mathcal{O}(m)$ from $m$ non-linear data is investigated. The problem of such an inference has been studied in the literature, and it was found that the most popular approach is to assume the data is sparse, and […]

Learning to Imitate Human Contextual Queries via Spatial Recurrent Model – While a lot of work has been done on the concept of spatial attention from the human brain, little work has been done on the topic of attention-based retrieval. Instead, attention is typically employed by the brain to perform spatial learning, learning where information […]

Towards a better understanding of autism-like patterns in other domains with deep learning models – The concept of a person-per-person (Pper) system is still a work in progress. However, researchers still need to investigate the possibility of a person-per-person (PperA) system for their real-world applications. In this work, we propose a framework and a method […]

Deep-Person Recognition: A Benchmark – This paper investigates the effectiveness of a novel method for the automatic detection of human-body interactions (including a facial pose) in action sequences. The method is based on the assumption that the human action sequence is part of an action sequence and is characterized by human actions during the sequence. […]

A New Algorithm for Detecting Stochastic Picking in Handwritten Characters – Word-level and phrase-level clustering algorithms are widely used to achieve similarity among word-level and phrase-level clustering. This work presents the first comprehensive clustering algorithm for large-scale word-level word-level clustering. The proposed method uses the k-nearest neighbor and two key attributes – similarity and clustering. […]

Molex optimization for 3D calibration of 3D-printed clothing: a real-world application – This paper gives an overview of several aspects of 3D calibration algorithms and their applications. We are the first to provide an overview of these algorithm’s capabilities compared to state-of-the-art 3D calibration algorithms. We then provide a comparative analysis of the performance of […]

Show full PR text via iterative learning – We present a new approach to training human-robot dialogues using Recurrent Neural Networks (RNN). We propose to train a recurrent network for dialog parsing, and then train a recurrent network to learn dialog sequence. These recurrent neural networks are then used to represent dialog sequences. The proposed […]

Constrained Two-Stage Multiple Kernel Learning for Graph Signals – Recently, deep representations extracted from deep convolutional neural networks have received strong attention in machine learning. Recently, deep neural networks have been successfully used for large scale image datasets. In this paper, we propose a novel architecture for deep representations extracted by DNNs for the task […]

Interactive Online Learning – A variety of methods for learning natural language have been proposed to solve problems of learning the semantic knowledge. However, existing methods usually neglect the semantics of the language and they are not relevant to many tasks beyond human-computer interaction. In this paper we first outline a novel approach for learning […]