[PDF] Top 20 A SURVEY ON DEEP LEARNING TECHNIQUES FOR BIG DATA IN BIOMETRICS

... two deep learning approaches. The first approach makes use of learning suitable convolutional network architectures for each domain and the second approach focuses on learning the weights of ... See full document

... Deep learning is an emerging research area in machine learning and pattern recognition ...field. Deep learning refers to machine learning techniques that use supervised or ... See full document

... OF DEEP MODELS TO PROBLEMS IN VISION AND ROBOTICS The preceding overview of machine learning applications in robotics will highlight five major areas where considerable impacts have been made by robotic ... See full document

... the deep belief network (DBN) and long short-term memory (LSTM) to predict urban traffic flow considering the impact of ...the deep learning predictors have better accuracy than existing predictors ... See full document

... incomplete data problem, in which certain data field values or features are missing, exists in a wide range of domains with the emerging big data, which may be caused by different realities, ... See full document

... a Deep Learning neural network with billions of parameters using tens of thousands of CPU cores, in the context of speech recognition and computer ...supports data parallelism, where multiple ... See full document

... Extreme Learning Machine (𝐸 𝐿𝑀) [20] was proposed for big data ...weak learning ability for the updated large-scale training ...training data subset and then updated the matrix ... See full document

... more data than ever ...sensor-generated data arriving at a terabyte and even zeta byte scale, new science and insights can be discovered from the highly detailed and domain-specific information which can ... See full document

... of data as due to diverse applica- tions of computers in all domain ...scale data, which also include person specific private and sensitive data like gender, zip code, disease, caste, shopping cart, ... See full document

... Big data comprises massive sensor data, raw and semi-structured log data of IT industries and the exploded quantity of data from social ...this data include high-volume sensor ... See full document

... on big data to avoid ...training data. Unfor- tunately, many application domains do not have access to big data, such as medical image ...This survey focuses on Data ... See full document

... Machine learning Technique has been introduced by some of the ...different techniques specific to the context has been used ...on deep learning and observed several unstructured, noisy, ... See full document

... the data processing methods are not suitable for handling voluminous and diversified data gather via IoT ...devices. Deep Learning (DL), is one of the most prominent methods in machine ... See full document

... Big Data Analysis can be performed on structured, unstructured as well as semi-structured ...[6]Structured data is data whose elements are addressable for effective ...all data which ... See full document

... in big data. They summarized a survey of existing clustering algorithms and provided a comparison, both from an empirical and theoretical ...valid big data ... See full document

... A blockchain is actually a growing list of block that consist the details of a transaction, which are linked by cryptographic methods. It was introduced when Bitcoin faced the double-spending problem [5]. Each block in ... See full document

... in data mining application domains for identifying relationship patterns, rules, and ...graph-structured data for the above applications needs a scalable system that can process large amounts of data ... See full document

... eep Learning has been forged accordingly for various Natural language processing problems and outperforms with marvelous results in semantics, modeling of sentences, classification, prediction, and other NLP ... See full document

... Aside from the last term, Eq. (3) resembles the perceptron learning rule, however with an alternate articulation for delta. Here is the learning rate. On the off chance that it is excessively expansive, we ... See full document

... synchronous, data parallel ...and data from one ...and Data parallelism, a hybrid approach that uses dif- ferent paradigms during specific training phases, would benefit HPCC systems by allowing it ... See full document