Learning Semantic Representations for the Phrase Translation Model

This paper presents a novel semantic-based phrase translation model. A pair of source and target phrases are projected into continuous-valued vector representations in a low-dimensional latent semantic space, where their translation score is computed by the distance between the pair in this new space. The projection is performed by a multi-layer neural network whose weights are learned on parallel training data. The learning is aimed to directly optimize the quality of end-to-end machine translation results. Experimental evaluation has been performed on two Europarl translation tasks, English-French and German-English. The results show that the new semantic-based phrase translation model significantly improves the performance of a state-of-the-art phrase-based statistical machine translation sys-tem, leading to a gain of 0.7-1.0 BLEU points

Random Coordinate Descent Methods for Minimizing Decomposable Submodular Functions

Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solvable in polynomial time, but general purpose algorithms have high running times and are unsuitable for large-scale problems. Recent work have used convex optimization techniques to obtain very practical algorithms for minimizing functions that are sums of ``simple" functions. In this paper, we use random coordinate descent methods to obtain algorithms with faster linear convergence rates and cheaper iteration costs. Compared to alternating projection methods, our algorithms do not rely on full-dimensional vector operations and they converge in significantly fewer iterations

A Projection-Free Algorithm for Solving Support Vector Machine Models

In this thesis our goal is to solve the dual problem of the support vector machine (SVM) problem, which is an example of convex smooth optimization problem over a polytope. To this goal, we apply the conditional gradient (CG) method by providing explicit solution to the linear programming (LP) subproblem. We also describe the conditional gradient sliding (CGS) method that can be considered as an improvement of CG in terms of number of gradient evaluations. Even though CGS performs better than CG in terms of optimal complexity bounds, it is not a practical method because it requires the knowledge of the Lipschitz constant and also the number of iterations. As an improvement of CGS, we designed a new method, conditional gradient sliding with line search (CGS-ls) that resolves the issues in CGS method. CGS-ls requires $O(1/\sqrt{1/\epsilon})$ gradient evaluations and $O(1/\epsilon)$ linear optimization calls that achieves the optimal complexity bounds in CGS method. We also compare the performance of our method with CG and CGS methods as numerical results by experimenting them in dual problem of SVM for binary classification of two subsets of the MNIST hand-written digits dataset

Fast Machine Learning Method with Vector Embedding on Orthonormal Basis and Spectral Transform

This paper presents a novel fast machine learning method that leverages two techniques: Vector Embedding on Orthonormal Basis (VEOB) and Spectral Transform (ST). The VEOB converts the original data encoding into a vector embedding with coordinates projected onto orthonormal bases. The Singular Value Decomposition (SVD) technique is used to calculate the vector basis and projection coordinates, leading to an enhanced distance measurement in the embedding space and facilitating data compression by preserving the projection vectors associated with the largest singular values. On the other hand, ST transforms sequence of vector data into spectral space. By applying the Discrete Cosine Transform (DCT) and selecting the most significant components, it streamlines the handling of lengthy vector sequences. The paper provides examples of word embedding, text chunk embedding, and image embedding, implemented in Julia language with a vector database. It also investigates unsupervised learning and supervised learning using this method, along with strategies for handling large data volumes.Comment: update 9. Strategies for managing large data volumes with 9.1. Using incremental SV

Axis current damage identification method based on bispectral locally preserving projection

A bispectral locally-preserving projection fault identification method is proposed. Fault pattern recognition is performed using a support vector machine (SVM). The experimental results show that the method can effectively identify the current damage of the bearing shaft, and the classification accuracy of the bearing fault containing the shaft current damage can reach more than 96.25 %

Sensorless Synchronous Reluctance Motor Drives: A Projection Vector Approach for Stator Resistance Immunity and Parameter Adaptation

The paper presents a general projection vector framework for the analysis of flux and position observers applied to sensorless control of synchronous reluctance machines, with emphasis to parametric errors sensitivity. The stator resistance immunity property of Adaptive Projection vector for Position error estimation (APP) technique is demonstrated, in maximum torque per ampere (MTPA) conditions. Out of MTPA, additional resistance adaption is devised for accurate estimation of stator flux and torque. Alternatively, inductance adaptation might be preferred to resistance's, when dealing with inaccurate motor flux-maps. Inductance adaptation is shown to decrease the steady-state position error. All proposed APP observers with adaptation techniques are subjected to stability analysis. The merit and the feasibility of the proposed scheme is experimentally demonstrated on a 1.1 kW synchronous reluctance (SyR) machine test-bench