Candidate working set strategy based SMO algorithm in support vector machine

Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of this algorithm is the selection of the working set, which greatly affects the training speed. The feasible direction strategy for the working set selection can decrease the objective function, however, may augment to the total calculation for selecting the working set in each of the iteration. In this paper, a new candidate working set (CWS) Strategy is presented considering the cost on the working set selection and cache performance. This new strategy can select several greatest violating samples from Cache as the iterative working sets for the next several optimizing steps, which can improve the efficiency of the kernel cache usage and reduce the computational cost related to the working set selection. The results of the theory analysis and experiments demonstrate that the proposed method can reduce the training time, especially on the large-scale datasets.

Protein export in Plasmodium parasites : from the endoplasmic reticulum to the vacuolar export machine

It is somewhat paradoxical that the malaria parasite’s survival strategy involves spending almost all of its blood-stage existence residing behind a two-membrane barrier in a host red blood cell, yet giving considerable attention to exporting parasite-encoded proteins back across these membranes. These exported proteins are thought to play diverse roles and are crucial in pathogenic processes, such as re-modelling of the erythrocyte cytoskeleton and mediating the export of a major virulence protein known as Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), and in metabolic processes such as nutrient uptake and solute exchange. Despite these varied roles most exported proteins have at least one common link; they share a trafficking pathway that begins with entry into the endoplasmic reticulum and concludes with passage across the vacuole membrane via a proteinaceous translocon known as the Plasmodium translocon of exported proteins (PTEX). In this commentary we review recent advances in our understanding of this export pathway and suggest several models by which different aspects of the process may be interconnected.

Machine learning approaches for modeling spammer behavior

Spam is commonly known as unsolicited or unwanted email messages in the Internet causing potential threat to Internet Security. Users spend a valuable amount of time deleting spam emails. More importantly, ever increasing spam emails occupy server storage space and consume network bandwidth. Keyword-based spam email filtering strategies will eventually be less successful to model spammer behavior as the spammer constantly changes their tricks to circumvent these filters. The evasive tactics that the spammer uses are patterns and these patterns can be modeled to combat spam. This paper investigates the possibilities of modeling spammer behavioral patterns by well-known classification algorithms such as Naïve Bayesian classifier (Naive Bayes), Decision Tree Induction (DTI) and Support Vector Machines (SVMs). Preliminary experimental results demonstrate a promising detection rate of around 92%, which is considerably an enhancement of performance compared to similar spammer behavior modeling research.

Internet traffic classification using machine learning : a token-based approach

Due to the increasing unreliability of traditional port-based methods, Internet traffic classification has attracted a lot of research efforts in recent years. Quite a lot of previous papers have focused on using statistical characteristics as discriminators and applying machine learning techniques to classify the traffic flows. In this paper, we propose a novel machine learning based approach where the features are extracted from packet payload instead of flow statistics. Specifically, every flow is represented by a feature vector, in which each item indicates the occurrence of a particular token, i.e.; a common substring, in the payload. We have applied various machine learning algorithms to evaluate the idea and used different feature selection schemes to identify the critical tokens. Experimental result based on a real-world traffic data set shows that the approach can achieve high accuracy with low overhead.

Pareto archived simulated annealing for single machine job shop scheduling with multiple objectives

In this paper, the single machine job shop scheduling problem is studied with the objectives of minimizing the tardiness and the material cost of jobs. The simultaneous consideration of these objectives is the multi-criteria optimization problem under study. A metaheuristic procedure based on simulated annealing is proposed to find the approximate Pareto optimal (non-dominated) solutions. The two objectives are combined in one composite utility function based on the decision maker’s interest in having a schedule with weighted combination. In view of the unknown nature of the weights for the defined objectives, a priori approach is applied to search for the non-dominated set of solutions based on the Pareto dominance. The obtained solutions set is presented to the decision maker to choose the best solution according to his preferences. The performance of the algorithm is evaluated in terms of the number of non-dominated schedules generated and the proximity of the obtained non-dominated front to the true Pareto front. Results show that the produced solutions do not differ significantly from the optimal solutions.

ATM (cash machine) SFX

ATM (Cash Machine) sound effect.

An application of machine learning techniques for the classification of glaucomatous progression

This paper presents an application of machine learning to the problem of classifying patients with glaucoma into one of two classes:stable and progressive glaucoma. The novelty of the work is the use of new features for the data analysis combined with machine learning techniques to classify the medical data. The paper describes the new features and the results of using decision trees to separate stable and progressive cases. Furthermore, we show the results of using an incremental learning algorithm for tracking stable and progressive cases over time. In both cases we used a dataset of progressive and stable glaucoma patients obtained from a glaucoma clinic.

Graph matching : fast candidate elimination using machine learning techniques

Graph matching is an important class of methods in pattern recognition. Typically, a graph representing an unknown pattern is matched with a database of models. If the database of model graphs is large, an additional factor in induced into the overall complexity of the matching process. Various techniques for reducing the influence of this additional factor have been described in the literature. In this paper we propose to extract simple features from a graph and use them to eliminate candidate graphs from the database. The most powerful set of features and a decision tree useful for candidate elimination are found by means of the C4.5 algorithm, which was originally proposed for inductive learning of classication rules. Experimental results are reported demonstrating that effcient candidate elimination can be achieved by the proposed procedure.

K-Complex Detection Using a Hybrid-Synergic Machine Learning Method

Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection.