2019 Deep Learning Projects

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In unsupervised learning, the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points.IEEE Projects on Machine Learning Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

2018-2019 Machine Learning Projects for Mtech

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In unsupervised learning, the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs. Unsupervised learning algorithms are used to IEEE Projects on Machine Learningfind structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

2018-2019 Machine Learning Projects for Engineering Students

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In unsupervised learning, the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points.IEEE Projects on Machine Learning Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

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2018-2019 IEEE Projects on Deep Learning

2018-2019 IEEE Machine Learning Projects

2018-2019 IEEE Machine Learning Projects

IEEE Machine learning Data Science projects
  • 1.Dynamical Component Analysis (DYCA): Dimensionality Reduction for High-Dimensional Deterministic Time-Series


  • 2.Unsupervised Parsimonious Cluster-Based Anomaly Detection (PCAD)


  • 3.Enhanced Noisy Sparse Subspace Clustering via Reweighted L1-Minimization


  • 4.Evaluation of Loss Functions for Estimation of Latent Vectors from GAN


  • 5.Space-Time Extension of the MEM Approach for Electromagnetic Neuroimaging

  • machine leanring projects for final year students


    • Mtech Projects on Machine Learning

      Human footsteps can provide a unique behavioural pattern for robust biometric systems. We propose spatio-temporal footstep representations from floor-only sensor data in advanced computational models for automatic biometric verification. Our models deliver an artificial intelligence capable of effectively differentiating the fine-grained variability of footsteps between legitimate users (clients) and impostor users of the biometric system. The methodology is validated in the largest to date footstep database, containing nearly 20,000 footstep signals from more than 120 users. The database is organized by considering a large cohort of impostors and a small set of clients to verify the reliability of biometric systems.

    • IEEE Projects on Machine Learning

      Kernel support vector machines (SVMs) deliver state-of-the-art results in many real-world nonlinear classification problems, but the computational cost can be quite demanding in order to maintain a large number of support vectors. Linear SVM, on the other hand, is highly scalable to large data but only suited for linearly separable problems. In this paper, we propose a novel approach called low-rank linearized SVM to scale up kernel SVM on limited resources. Our approach transforms a nonlinear SVM to a linear one via an approximate empirical kernel map computed from efficient kernel low-rank decompositions. We theoretically analyze the gap between the solutions of the approximate and optimal rank.

    • Machine Learning Projects in Bangalore

      The abundance of image-level labels and the lack of large scale detailed annotations (e.g. bounding boxes, segmentation masks) promotes the development of weakly supervised learning (WSL) models. In this work, we propose a novel framework for WSL of deep convolutional neural networks dedicated to learn localized features from global image-level annotations. The core of the approach is a new latent structured output model equipped with a pooling function which explicitly models negative evidence, e.g. a cow detector should strongly penalize the prediction of the bedroom class. We show that our model can be trained end-to-end for different visual recognition tasks: multi-class and multi-label classification, and also structured average precision (AP) ranking. Extensive experiments highlight the relevance of the proposed method: our model outperforms state-of-the art results on six datasets.

    2018-2019 Machine Learning Projects in BangaloreIEEE Projects on Machine Learning

    Machine learning (ML) is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data", IEEE Projects on Machine Learningin order to make predictions or decisions without being explicitly programmed to perform the task.

    2018-2019 Machine Learning Projects in Bangalore

    2018-2019 IEEE Projects for CSE Machine Learning
    The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning,and focuses on exploratory data analysis through unsupervised learning.In its application across business problems, machine learning is also referred to as predictive analytics. IEEE Projects on Machine Learning Different from these approaches on fusion of stereo and activedepth sensors, our method focuses on generating high-resolutiondepth video with one color video (not stereo) and its corresponding low-resolution depth video. For that, we proposea weighted mode filtering (WMF) based on a joint histogram.The weight based on similarity measure between reference and neighboring pixels is used to construct the histogram, and a final solution is then determined by seeking a global mode on the histogram

    convolution filter based live Traffic sign Detection

    The 2D convolution is a fairly simple operation at heart: you start with a kernel, which is simply a small matrix of weights. This kernel “slides” over the 2D input data, performing an elementwise multiplication with the part of the input it is currently on, and then summing up the results into a single output pixel.
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    2018-2019 IEEE Projects on Machine Learning

    Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals;