Matching pursuit algorithms learn a function that is weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target function in the least-squares sense. Experimental result shows that it is an effective method, but the drawbacks are that this algorithm is not appropriate to online learning or estimating the strongly nonlinear functions....

The letter describes an online deep learning algorithm (ODL) for adaptive modulation and coding in massive MIMO. is based on a fully connected neural network, which initially trained the output of traditional then incrementally retrained by service feedback its output. We show advantage our solution over state-of-the-art Q-learning approach. provide system-level simulation results to support th...

This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without explicit regularization. We present a novel capacity independent approach to derive error bounds and convergence results for this algorithm. We show that, although the algorithm does not involve an explicit RKHS regularization term, choosing the step sizes appropriately c...

Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learning a ranking function in an online setting. Our algorithm is an extension of t...

We present a unified framework for online learning of structured classifiers that handles a wide family of convex loss functions, properly including CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family. For the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, it can be regarded as a d...

We present a unified framework for online learning of structured classifiers that handles a wide family of convex loss functions, properly including CRFs, structured SVMs, and the structured perceptron. We introduce a new aggressive online algorithm that optimizes any loss in this family. For the structured hinge loss, this algorithm reduces to 1-best MIRA; in general, it can be regarded as a d...