In this paper, we propose online algorithms for multiclass classification using partial labels. We propose two variant s of Perceptron called Avg Perceptron and Max Perceptron to deal with the par tial labeled data. We also propose Avg Pegasos and Max Pegasos, whic h are extensions of Pegasos algorithm. We also provide mistake bounds for Avg Perceptron and regret bound for Avg Pegasos. We show the effec tiveness of the proposed approaches by experimenting on various data sets and comparing them with the standard Perceptron and Pegasos.

We consider the online multiclass linear classification under the bandit feedback setting. Beygelzimer, P\'{a}l, Sz\"{o}r\'{e}nyi, Thiruvenkatachari, Wei, and Zhang [ICML'19] considered two notions of linear separability, weak and strong linear separability. When examples are strongly linearly separable with margin $\gamma$, they presented an algorithm based on Multiclass Perceptron with mistake bound $O(K/\gamma^2)$, where $K$ is the number of classes. They employed rational kernel to deal with examples under the weakly linearly separable condition, and obtained the mistake bound of $\min(K\cdot 2^{\tilde{O}(K\log^2(1/\gamma))},K\cdot 2^{\tilde{O}(\sqrt{1/\gamma}\log K)})$. In this paper, we refine the notion of weak linear separability to support the notion of class grouping, called group weak linear separable condition. This situation may arise from the fact that class structures contain inherent grouping. We show that under this condition, we can also use the rational kernel and obtain the mistake bound of $K\cdot 2^{\tilde{O}(\sqrt{1/\gamma}\log L)})$, where $L\leq K$ represents the number of groups.

Despite the success of deep learning in domains such as image, voice, and graphs, there has been little progress in deep representation learning for domains without a known structure between features. For instance, a tabular dataset of different demographic and clinical factors where the feature interactions are not given as a prior. In this paper, we propose Group-Connected Multilayer Perceptron (GMLP) networks to enable deep representation learning in these domains. GMLP is based on the idea of learning expressive feature combinations (groups) and exploiting them to reduce the network complexity by defining local group-wise operations. During the training phase, GMLP learns a sparse feature grouping matrix using temperature annealing softmax with an added entropy loss term to encourage the sparsity. Furthermore, an architecture is suggested which resembles binary trees, where group-wise operations are followed by pooling operations to combine information; reducing the number of groups as the network grows in depth. To evaluate the proposed method, we conducted experiments on five different real-world datasets covering various application areas. Additionally, we provide visualizations on MNIST and synthesized data. According to the results, GMLP is able to successfully learn and exploit expressive feature combinations and achieve state-of-the-art classification performance on different datasets.

In this longish post, I have tried to explain Deep Learning starting from familiar ideas like machine learning. This approach forms a part of my forthcoming book. You can connect with me on Linkedin to know more about the book. I have used this approach in my teaching. It is based on'learning by exception,' i.e. understanding one concept and it's limitations and then understanding how the subsequent concept overcomes that limitation. We thus develop a chain of thought that starts with linear regression and extends to multilayer perceptron (Deep Learning).

Check out the entire Machine Learning Playlist: https://bit.ly/2NG9tK4

Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance, security, etc. However, existing models are hard to simultaneously satisfy the above two properties. In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure. To address the challenge of efficiently learning the non-differentiable CRS model, we propose a novel neural network architecture, Multilayer Logical Perceptron (MLLP), which is a continuous version of CRS. Using MLLP and the Random Binarization (RB) method we proposed, we can search the discrete solution of CRS in continuous space using gradient descent and ensure the discrete CRS acts almost the same as the corresponding continuous MLLP . Experiments on 12 public data sets show that CRS outperforms the state-of-the-art approaches and the complexity of the learned CRS is close to the simple decision tree. Introduction Relying on strong ability of data modeling, machine learning, especially deep learning, becomes the main paradigm for decision-making systems (Goodfellow et al. 2016; Doshi-V elez and Kim 2017). The decision-making systems have widespread usage in important areas such as medicine, finance, politics, as well as law, where people need the explanations why decisions are made to ensure their safety and protect their rights (Goodman and Flaxman 2016; Lipton 2016). As a result, the demand for the transparency of machine learning methods is increasing, which is crucial for earning the trust of users (Doshi-V elez and Kim 2017) and reducing potential risks and bugs (Chu et al. 2018). However, most of the machine learning models can hardly ensure good predictive ability and transparency at the same time, and sacrificing transparency for good performance could result in serious consequences.

Complete Video Series on "Hands on Artificial Intelligence, Machine Learning & Deep Learning using TensorFlow, Keras and Python" I am Gulshan Yadav. An Embedded Systems Development professional with nearly 13 of years R&D experience in design & development of Embedded products in Automotive, IOT and AI domain. About this Video: -------------------------- This video will explain 1. What is Perceptron / Artificial Neuron 2. Basic Building Blocks of Perceptron 3. How Pereptron works? 4. Why Bias is used in Perceptron and Artificial Neural Networks Social Links: Twitter: https://twitter.com/techopcode

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia.

In the early days of research on Artificial Intelligence, Frank Rosenblatt, a scientist at Cornell University in the United States, invented what he called the "perceptron". The perceptron was an algorithm designed to classify objects it was shown and an ancestor of modern Artificial Intelligence. When Rosenblatt became a little boastful at a press conference in 1958, the New York Times picked up on it and went a little overboard with excitement. "NEW NAVY DEVICE LEARNS BY DOING; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser", read the title of an article. The Navy said the perceptron would be the first non-living mechanism "capable of receiving, recognizing and identifying its surroundings without any human training or control" Does this tone sound familiar?

Many of the biggest advances in AI are driven by artificial neural networks. Artificial Neural Networks (ANNs) are the connection of mathematical functions joined together in a format inspired by the neural networks found in the human brain. These ANNs are capable of extracting complex patterns from data, applying these patterns to unseen data to classify/recognize the data. In this way, the machine "learns". That's a quick rundown on neural networks, but let's take a closer look at neural networks to better understand what they are and how they operate.